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Even though research has repeatedly shown that non-cash incentives can be effective, cash incentives are the de facto standard in crowdsourcing contests. In this multi-study research, we quantify ideators' preferences for non-cash incentives and investigate how allowing ideators to self-select their preferred incentive—offering ideators a choice between cash and non-cash incentives—affects their creative performance. We further explore whether the market context of the organization hosting the contest—social (non-profit) or monetary (for-profit)—moderates incentive preferences and their effectiveness. We find that individuals exhibit heterogeneous incentive preferences and often prefer non-cash incentives, even in for-profit contexts. Offering ideators a choice of incentives can enhance creative performance. Market context moderates the effect of incentives, such that ideators who receive non-cash incentives in for-profit contexts tend to exert less effort. We show that heterogeneity of ideators' preferences (and the ability to satisfy diverse preferences with suitably diverse incentive options) is a critical boundary condition to realizing benefits from offering ideators a choice of incentives. We provide managers with guidance to design effective incentives by improving incentive-preference fit for ideators. [ABSTRACT FROM AUTHOR]

Firms often use crowdsourcing contests to develop marketing ideas and solutions. Despite the prevalence—and unique aspects—of marketing ideation crowdsourcing contests (MICCs), there has been little examination of these contests' shareholder wealth implications. Adopting a signaling perspective, the authors conduct an event study of 508 MICC announcements and find that they are associated with higher returns, but also higher idiosyncratic risk, indicating that investors hold a mixed view of such contests. Further, the authors consider how contest design factors and firm marketing resources may signal the cultivation of intellectual and relational market-based assets to shape their stock market impact, providing firms guidance to better design their MICCs. Specifically, they find that returns are enhanced when firms use professional (vs. general public) contests, specifically scoped contests, and contests using crowd judging (vs. expert panels), and for firms with stronger marketing capabilities. However, brand factors have mixed effects on returns, with a brand's relevant stature having a positive effect and its energized differentiation having a negative effect on returns. Product MICCs and generally scoped contests heighten the negative effects on risk, whereas marketing resources have no impact. Results offer implications for practitioners, including the finding that many MICC design choices commonly used in practice (i.e., general public contests and expert panels) are viewed less favorably by investors. [ABSTRACT FROM AUTHOR]

Amazon Mechanical Turk Workers lack accountability andredress when they are managed at scale, and by code. Report compiled by Amazon Mechanical Turk workers and academic collaborators, submitted to White House Office of Science and Technology Policy.

As different crowdsourcing routines (metaphorically labeled as "fishing" and "hunting" in this study) are available to address highly technical problems, solution-seeking organizations need to mindfully design, select, and deploy crowdsourcing routines that account for the behavior and motivation of experts. Drawing on a survey involving 260 experts in science, technology, engineering, and math fields, we found that elite experts (individuals with seniority, aged over 40, and a proven track record in the field with numerous publications and patents) are generally less inclined to search for crowdsourcing open calls and prefer to be contacted by solution seekers. In contrast, non-elite experts (early career experts, aged under 40, and with fewer patents and publications) actively search to find open calls. Regarding their motivational underpinnings, our findings suggest that elite experts are motivated more by non-financial incentives than non-elite experts. Furthermore, as the frequency with which they are contacted increases, non-elite experts tend to prefer more non-financial over financial incentives. These results indicate that the fishing crowdsourcing routine generally elicits solutions from unproven, non-elite experts who demand more financial rewards. However, the hunting routine taps a pool of elite experts with proven capabilities who are less financially oriented and thus may provide better, yet less expensive solutions. [ABSTRACT FROM AUTHOR]

An Achille's heel of lower-rung density-functional approximations is that the highest-occupied-molecular-orbital energy levels of anions, known to be stable or metastable in nature, are often found to be positive in the worst case or above the lowest-unoccupied-molecular-orbital levels on neighboring complexes that are not expected to accept charge. A trianionic example, [ Cr ( C 2 O 4 ) 3 ] 3 − , is of interest for constraining models linking Cr isotope ratios in rock samples to oxygen levels in Earth's atmosphere over geological timescales. Here we describe how crowd sourcing can be used to carry out self-consistent Fermi–Löwdin–Orbital-Self-Interaction corrected calculations (FLOSIC) on this trianion in solution. The calculations give a physically correct description of the electronic structure of the trianion and water. In contrast, uncorrected local density approximation (LDA) calculations result in approximately half of the anion charge being transferred to the water bath due to the effects of self-interaction error. Use of group-theory and the intrinsic sparsity of the theory enables calculations roughly 125 times faster than our initial implementation in the large N limit reached here. By integrating charge density densities and Coulomb potentials over regions of space and analyzing core-level shifts of the Cr and O atoms as a function of position and functional, we unambiguously show that FLOSIC, relative to LDA, reverses incorrect solute-solvent charge transfer in the trianion-water complex. In comparison to other functionals investigated herein, including Hartree–Fock and the local density approximation, the FLOSIC Cr 1s eigenvalues provide the best agreement with experimental core ionization energies. [ABSTRACT FROM AUTHOR]

Since we wrote "Crowdsourcing as a solution to distant search" a decade ago, enthusiasm for crowdsourcing's capacity to produce remarkable solutions to some problems has continued to grow, and profiting from crowdsourced solutions has become a strategic goal for more and more firms. Crowdsourcing research has progressed impressively, with more progress made in researching the phenomenon than in theorizing about it. We are deeply honored by the 2022 Decade Award. In this manuscript, we reflect on the factors that led to this progress in crowdsourcing research, and how the theoretical insights from our paper––e.g., markets in the hierarchies-markets dichotomy are made up of markets with ex ante contracts and crowdsourcing (markets with no ex ante contracts)––may have influenced that progress. Also, because profits have become the ultimate goal of a large number of the seekers of solutions through crowdsourcing, we present an outline of a framework for exploring the impact of crowdsourcing on a seeker's profitability. The framework builds on insights from the paper and new constructs in crowdsourcing such as artificial intelligence (AI), the crowdsourcing process, revenue models, complementary assets, and organizing to minimize crowdsourcing disadvantages that we have added. We conclude with suggestions for future research. [ABSTRACT FROM AUTHOR]

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB;

Ambient Ionization Mass Spectrometry (AI-MS) techniques have revolutionized analytical chemistry by enabling rapid analysis of samples under atmospheric conditions with minimal to no preparation. In this study, the optimization of a cold atmospheric plasma for the analysis of food and pharmaceutical samples, liquid and solid, using a Heat-Assisted Dielectric Barrier Discharge Ionization (HA-DBDI) source is described. A significant enhancement in analyte signals was observed when a heating element was introduced into the design, potentially allowing for greater sensitivity. Furthermore, the synergy between the inlet temperature of the mass spectrometer and the heating element allows for precise control over the analytical process, leading to improved detection sensitivity and selectivity. Incorporating computational fluid dynamic (CFD) simulations into the study elucidated how heating modifications can influence gas transport properties, thereby facilitating enhanced analyte detection and increased signal intensity. These findings advance the understanding of HA-DBDI technology and provide valuable insights for optimizing AI-MS methodologies for a wide range of applications in food and pharmaceutical analysis. [ABSTRACT FROM AUTHOR]

Online labor markets have gained significant importance in recent years, drawing considerable attention in academia and practice. These platforms enable workers worldwide to sell their labor services to a global pool of clients. However, the challenge lies in motivating workers effectively to enhance their productivity. To address this issue, we employ the self—determination theory and present a model that elucidates motivation's impact on productivity across various payment schemes. Additionally, we leverage the psychological trait theory and its suggested taxonomy to explore how compensation policies in online labor markets affect incentives differently based on individual differences. Our experiment tests predictions from a formal labor supply and productivity model for workers with varying compensation levels. The results indicate that intrinsic workers exhibit higher productivity when bonus rewards are introduced. Furthermore, our study confirms the presence of heterogeneous personality effects, emphasizing that increased worker productivity is primarily associated with conscientiousness and agreeableness traits. These findings illuminate the intricate mechanisms governing worker motivation and engagement in paid crowdsourcing environments. They provide valuable theoretical and managerial insights for researchers and crowdsourcing practitioners aiming to enhance worker productivity in online tasks. [ABSTRACT FROM AUTHOR]

Intraspecific variation in camouflage is common in animals. Sexual dimorphism in camouflage is less common and, where observed, attributed to trade-offs between natural selection for predator avoidance and sexual selection for conspicuous mating signals. Here we report on variation in putatively cryptic ventral hindwing patterns in the American snout butterfly, Libytheana carinenta. We use field surveys and crowdsourced data to characterize three morphs. One is found in both sexes, one is male specific, and one is female specific. The sex-specific morphs constitute a sexually dimorphic set whose frequencies change together in time. Field surveys indicate that butterflies in southern Arizona transition from midsummer dominance of the sexually monomorphic pattern to early-fall dominance of the sexually dimorphic set. Crowdsourced data indicate that the sexually dimorphic set dominates in early spring, transitioning later into a mixture of morphs dominated by the monomorphic pattern, with the dimorphic set rising in frequency again in late fall. We discuss this unique pattern of camouflage variation with respect to contemporary theory on animal coloration. [ABSTRACT FROM AUTHOR]

Infants are immersed in a world of sounds from the moment their auditory system becomes functional, and experience with the auditory world shapes how their brain processes sounds in their environment. Across cultures, speech and music are two dominant auditory signals in infants' daily lives. Decades of research have repeatedly shown that both quantity and quality of speech input play critical roles in infant language development. Less is known about the music input infants receive in their environment. This study is the first to compare music input to speech input across infancy by analyzing a longitudinal dataset of daylong audio recordings collected in English‐learning infants' home environments, at 6, 10, 14, 18, and 24 months of age. Using a crowdsourcing approach, 643 naïve listeners annotated 12,000 short snippets (10 s) randomly sampled from the recordings using Zooniverse, an online citizen‐science platform. Results show that infants overall receive significantly more speech input than music input and the gap widens as the infants get older. At every age point, infants were exposed to more music from an electronic device than an in‐person source; this pattern was reversed for speech. The percentage of input intended for infants remained the same over time for music while that percentage significantly increased for speech. We propose possible explanations for the limited music input compared to speech input observed in the present (North American) dataset and discuss future directions. We also discuss the opportunities and caveats in using a crowdsourcing approach to analyze large audio datasets. A video abstract of this article can be viewed at https://youtu.be/lFj%5fsEaBMN4 Research Highlights: This study is the first to compare music input to speech input in infants' natural home environment across infancy.We utilized a crowdsourcing approach to annotate a longitudinal dataset of daylong audio recordings collected in North American home environments.Our main results show that infants overall receive significantly more speech input than music input. This gap widens as the infants get older.Our results also showed that the music input was largely from electronic devices and not intended for the infants, a pattern opposite to speech input. [ABSTRACT FROM AUTHOR]

Crowdsourcing is a powerful technique for accomplishing tasks that are difficult for machines but easy for humans. However, ensuring the quality of the workers who participate in the task is a major challenge. Most of the existing studies have focused on selecting suitable workers based on their attributes and the task requirements, while neglecting the requesters' characteristics as a key factor in the crowdsourcing process. In this paper, we address this gap by considering the requesters' preferences and behavior in crowdsourcing systems with competition, where the requester chooses only one worker's contribution as the final answer. A model is proposed in which the requesters' characteristics are taken into consideration when finding suitable workers. Also, we propose new definitions for clarity and the fairness of requesters and propose models and formulations to employ them, alongside task and workers' attributes, to find more suitable workers. We have evaluated the efficacy of our proposed model by analyzing a real-world dataset and compared it with two current state-of-the-art approaches. Our results demonstrate the superiority of our proposed method in assigning the most suitable workers. [ABSTRACT FROM AUTHOR]

The recent boom in crowdsourcing has opened up a new avenue for utilizing human intelligence in the realm of data analysis. This innovative approach provides a powerful means for connecting online workers to tasks that cannot effectively be done solely by machines or conducted by professional experts due to cost constraints. Within the field of social science, four elements are required to construct a sound crowd—Diversity of Opinion, Independence, Decentralization and Aggregation. However, while the other three components have already been investigated and implemented in existing crowdsourcing platforms, 'Diversity of Opinion' has not been functionally enabled yet. From a computational point of view, constructing a wise crowd necessitates quantitatively modeling and taking diversity into account. There are usually two paradigms in a crowdsourcing marketplace for worker selection: building a crowd to wait for tasks to come and selecting workers for a given task. We propose similarity-driven and task-driven models for both paradigms. Also, we develop efficient and effective algorithms for recruiting a limited number of workers with optimal diversity in both models. To validate our solutions, we conduct extensive experiments using both synthetic datasets and real data sets. [ABSTRACT FROM AUTHOR]

Lack of incentive strategies within the health crowdsourcing platform has resulted in the loss of patient innovation and the failure in the diffusion of patients' innovations. To solve this problem, we compared four incentive strategies: (i) without incentive, (ii) cost‐sharing incentive, (iii) revenue‐sharing incentive, and (iv) a hybrid incentive strategy combining cost‐sharing and revenue‐sharing. Our analysis indicates that the innovation guidance effect is at its lowest level without incentive strategies, and the hybrid incentive strategy can maximize both innovation enthusiasm and profits. Furthermore, there exists a threshold for revenue‐sharing ratio, making the incentive effect of revenue‐sharing strategy better than that of the cost‐sharing strategy. Finally, short‐sighted thinking among platform participants will reduce the innovation enthusiasm of altruistic patients and the incentive investment of the platform. This study provides guiding principles for the health crowdsourcing platform to regulate online patient innovation solutions. [ABSTRACT FROM AUTHOR]

Crowdsourcing holds broad applications in information acquisition and dissemination, yet encounters challenges pertaining to data quality assessment and user reputation management. Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores, thereby elevating the quality and dependability of crowdsourced data. However, these mechanisms face several challenges in traditional crowdsourcing systems: 1) platform security lacks robust guarantees and may be susceptible to attacks; 2) there exists a potential for large-scale privacy breaches; and 3) incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations, occasionally lacking a dedicated reputation update module. This paper introduces a reputation update scheme tailored for crowdsourcing, with a focus on proficiently overseeing participant reputations and alleviating the impact of malicious activities on the sensing system. Here, the reputation update scheme is determined by an Empirical Cumulative distribution-based Outlier Detection method (ECOD). Our scheme embraces a blockchain-based crowdsourcing framework utilizing a homomorphic encryption method to ensure data transparency and tamper-resistance. Computation of user reputation scores relies on their behavioral history, actively discouraging undesirable conduct. Additionally, we introduce a dynamic weight incentive mechanism that mirrors alterations in participant reputation, enabling the system to allocate incentives based on user behavior and reputation. Our scheme undergoes evaluation on 11 datasets, revealing substantial enhancements in data credibility for crowdsourcing systems and a reduction in the influence of malicious behavior. This research not only presents a practical solution for crowdsourcing reputation management but also offers valuable insights for future research and applications, holding promise for fostering more reliable and high-quality data collection in crowdsourcing across diverse domains. [ABSTRACT FROM AUTHOR]

Purpose: T1 mapping is a widely used quantitative MRI technique, but its tissue‐specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well‐established inversion‐recovery T1 mapping technique, using acquisition details from a seminal T1 mapping paper on a standardized phantom and in human brains. Methods: The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open‐source platforms. Intersubmission and intrasubmission comparisons were performed. Results: Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org. Conclusion: The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo. [ABSTRACT FROM AUTHOR]

Resurgence can be defined as increases in previously reinforced and subsequently extinguished target responding when conditions for an alternative response worsen. Worsening of alternative conditions, such as extinction, has been linked to relapse of clinically relevant behavior. Preclinical researchers have evaluated whether punishing target responses while differentially reinforcing an alternative response could reduce resurgence when conditions are worsened with extinction, with mixed results. In the present investigation, we systematically replicated this line of research with human participants recruited via crowdsourcing, using response cost as punishment. During Phase 1, we reinforced target responses with 100 points per delivery, exchangeable for money. During Phase 2, we reinforced alternative responses, discontinued point reinforcement for target responses, and parametrically manipulated across groups the magnitude of point loss (1, 100, 320, or 1,000 points) contingent on target responses. During Phase 3, we tested for resurgence by extinguishing target and alternative responses. Added punishment systematically decreased target responding during Phase 2 but did not influence resurgence during Phase 3. With a meta‐analysis, we compared our findings with existing research examining a range of punishers and species. The results of the meta‐analysis comport with the present findings, suggesting that the inclusion of punishment reduces target responding during DRA but, overall, has no systematic effects on resurgence. [ABSTRACT FROM AUTHOR]

The present paper is concerned with a fourth‐order Allen–Cahn model with logarithmic potential and mass source that describes the process of phase separation in two‐component systems accompanied by a flux of material. The existence of a global weak solution is obtained under appropriate hypotheses on the source term. Furthermore, we study its Cahn–Hilliard limit as a small parameter goes to zero. The main difficulty in the mathematical analysis of the model lies in the presence of the source term that leads to the nonconservation of mass, contrary to the original Cahn–Hilliard theory. [ABSTRACT FROM AUTHOR]

Crowdsourcing deals with solving problems by assigning them to a large number of non-experts called crowd using their spare time. In these systems, the final answer to the question is determined by summing up the votes obtained from the community. The popularity of these systems has increased by facilitating access for community members through mobile phones and the Internet. One of the issues raised in crowdsourcing is how to choose people and how to collect answers. Usually, users are separated based on their performance in a pre-test. Designing the pre-test for performance calculation is challenging; The pre-test questions should be selected to assess characteristics in individuals that are relevant to the main questions. One of the ways to increase the accuracy of crowdsourcing systems is by considering individuals’ cognitive characteristics and decision-making models to form a crowd and improve the estimation of their answer accuracy to questions. People can estimate the correctness of their responses while making a decision. The accuracy of this estimate is determined by a quantity called metacognition ability. Metacoginition is referred to the case where the confidence level is considered along with the answer to increase the accuracy of the solution. In this paper, by both mathematical and experimental analysis, we would answer the following question: Is it possible to improve the performance of a crowdsourcing system by understanding individuals’ metacognition and recording and utilizing users’ confidence in their answers? [ABSTRACT FROM AUTHOR]

From the discussion of "posttruth" in 2016 to the "infodemic" in 2020, online rumors seem to have become more rampant, harmful, and harder to be debunked. This article examines Cofacts, a Taiwan-based fact-checking service that combines a chatbot and a database of fact-checked responses provided by volunteers to help debunk rumors circulated on the messaging app LINE. I argue that Cofacts's crowdsourcing approach joins what Donna Haraway calls embodied objectivity that insists on "the particularity and embodiment of all vision" to challenge the conventional fact-checking practice that presumes singularity, disembodied objectivity, and authority. Underpinning Cofacts's fight against online rumors is the intergenerational conflicts that are ingrained in different life experiences, beliefs and values, and expectations of what a good life is. By taking up a technological solution that emphasizes openness, Cofacts opens a space for digital natives to contest what fact is and claim the power of speaking from their parents and the patriarchal society on the one hand and to forge new connections of care and reinitiate conversations that have been barred by the invisible walls of chat rooms and the widening gap of values and beliefs between generations on the other hand. [ABSTRACT FROM AUTHOR]

Parliaments will eventually not evade the digital evolution of every institution to become data-driven organisations. This study examines the Hellenic Optical Character Recognition (OCR) Team, a pioneering crowdsourcing initiative aimed at processing and analysing parliamentary data. Under certain conditions, crowdsourcing, in other words, the power of the people, can be appropriately channelled and exploited to support representative institutions and their societal stakeholders in managing their change processes. Based on survey findings, this research highlights the profiles and motivations of participants, identifies best practices for crowdsourcing in parliamentary contexts, and demonstrates the initiative's impact on improving data accessibility and transparency. The results suggest that the Hellenic OCR Team significantly contributes to the digital evolution of parliamentary functions and promotes greater public involvement in the legislative process. [ABSTRACT FROM AUTHOR]

The expanding use of community science platforms has led to an exponential increase in biodiversity data in global repositories. Yet, understanding of species distributions remains patchy. Biodiversity data from social media can potentially reduce the global biodiversity knowledge gap. However, practical guidelines and standardized methods for harvesting such data are nonexistent. Following data privacy and protection safeguards, we devised a standardized method for extracting species distribution records from Facebook groups that allow access to their data. It involves 3 steps: group selection, data extraction, and georeferencing the record location. We present how to structure keywords, search for species photographs, and georeference localities for such records. We further highlight some challenges users might face when extracting species distribution data from Facebook and suggest solutions. Following our proposed framework, we present a case study on Bangladesh's biodiversity—a tropical megadiverse South Asian country. We scraped nearly 45,000 unique georeferenced records across 967 species and found a median of 27 records per species. About 12% of the distribution data were for threatened species, representing 27% of all species. We also obtained data for 56 DataDeficient species for Bangladesh. If carefully harvested, social media data can significantly reduce global biodiversity knowledge gaps. Consequently, developing an automated tool to extract and interpret social media biodiversity data is a research priority. [ABSTRACT FROM AUTHOR]

This paper explicates blame as a psychological construct in communication processes, with a focus on its underlying structure and its mediating role between message characteristics and the public's issue engagement. Data were collected from Amazon Mechanical Turk (N = 373) via a Web-based experiment, where we manipulated a news story about a child suffering an obesity-related health risk (asthma attack vs. heart attack) with different levels of preventability (high vs. low) and severity (high vs. low). Findings showed that blame should best be conceptualized and operationalized as a latent construct comprising both cognitive and affective components. Blame mediated message effects on social responses related to obesity prevention, including punitive attitudes, policy support, and intentions toward interpersonal communication and civic participatory behaviors. [ABSTRACT FROM AUTHOR]

Out‐of‐hospital cardiac arrest (OHCA) is a global public health problem, with survival rates remaining low at around 10% or less despite widespread cardiopulmonary resuscitation (CPR) training and availability of automated external defibrillators (AEDs). This is partly due to the challenges of knowing when and where a sudden OHCA occurs and where the nearest AED is located. In response, countries around the world have begun to use network technology‐based smartphone applications. These applications are activated by emergency medical service dispatchers and alert preregistered volunteer first responders (VFRs) to nearby OHCAs using Global Positioning System localization. Accumulating evidence, although mostly from observational studies, shows their effectiveness in increasing the rate of bystander CPR, defibrillation, and patient survival. Current guidelines recommend the use of these VFR alerting systems, and the results of ongoing randomized trials are awaited for further dissemination. This article also proposed the concept of a life‐saving mobile network (LMN), which uses opportunistic network and wireless sensor network technologies to create a dynamic mesh network of potential victims, rescuers, and defibrillators. The LMN works by detecting a fatal arrhythmia with a wearable sensor device, localizing the victim and the nearest AED with nearby smartphones, and notifying VFRs through peer‐to‐peer communication. While there are challenges and limitations to implementing the LMN in society, this innovative network technology would reduce the tragedy of sudden cardiac death from OHCA. [ABSTRACT FROM AUTHOR]

Online crowdsourcing platforms have rapidly become a popular source of data collection. Despite the various advantages these platforms offer, there are substantial concerns regarding not only data validity issues, but also the ethical, societal, and global ramifications arising from the prevalent use of online crowdsourcing platforms. This paper seeks to expand the dialogue by examining both the "internal" aspects of crowdsourcing research practices, such as data quality issues, reporting transparency, and fair compensation, and the "external" aspects, in terms of how the widespread use of crowdsourcing data collection shapes the nature of scientific communities and our society in general. Online participants in research studies are informal workers who provide labor in exchange for remuneration. The paper thus highlights the need for researchers to consider the markedly different political, economic, and socio-cultural characteristics of the Global North and the Global South when undertaking crowdsourcing research involving an international sample; such consideration is crucial for both increasing research validity and mitigating societal inequities. We encourage researchers to scrutinize the value systems underlying this popular data collection research method and its associated ethical, societal, and global ramifications, as well as provide a set of recommendations regarding the use of crowdsourcing platforms. [ABSTRACT FROM AUTHOR]

Processing large and heterogeneous numbers of ideas submitted to crowdsourcing contests is a regular challenge for idea evaluators. The aim of this study is to investigate a potential use case for AI-based innovation management and to extend the knowledge of using automated novelty detection in idea evaluation processes. AI-based language models can automatically allocate short texts according to their semantic similarity in an embedded space. We represent the semantic content of crowdsourced ideas with the three contemporary text embeddings – Doc2Vec, SBERT, and GPT-3-based Ada Similarity – and compute their semantic distance to different reference sets using different novelty detection algorithms. We then compare the algorithm-generated scores with human novelty assessments to validate them. While selected novelty scores based on text embeddings correlate with humans, our results show that scores based on SBERT embeddings best match human novelty assessments. We also find that AI-based novelty detection approaches perform better for ideas below the median word count and when compared to a set of existing solutions, suggesting that the chosen language model is not the only factor influencing the applicability of the proposed approach. Furthermore, the study highlights important features and limitations of automatically generated novelty scores that need to be considered when complementing evaluators searching for new ideas in crowdsourcing contests and beyond. [ABSTRACT FROM AUTHOR]

Internal crowdsourcing offers new opportunities for ideas to gain legitimacy in idea development as not only managers but also experts are able to provide feedback to improve and subsequently accept ideas for further realisation. Given this far limited knowledge about how ideas gain legitimacy in idea development, this paper focuses on the influence of feedback, a main source of idea legitimacy in internal crowdsourcing. More specifically, this study aims to explore how feedback from different feedback providers (i.e., managers and experts) influences idea selection in internal crowdsourcing idea development. Based on text mining methods including sentiment, topic-model, and expert identification analyses, results reveal that feedback sentiment, feedback diversity and feedback amount play key roles in idea legitimisation. Specifically, these aspects of feedback influence idea acceptance in idea selection through the mediating role of idea revision in idea development and that this relationship is moderated by feedback providers. This study extends previous knowledge about the legitimisation process from the perspective of feedback in idea development by offering both a more comprehensive view, including different feedback aspects, and a more granular measurement of these, through the use of text analysis. Based on insights from the study, practical implications are presented for how to gain legitimacy from feedback for idea selection in internal crowdsourcing. [ABSTRACT FROM AUTHOR]

Besides the cusp, polar cap, and auroral oval, the nightside subauroral zone has also recently been reported as a source region of the ionospheric oxygen outflows. However, the detailed mass and energy sources of these ions remain open questions. Here, we address this issue from the perspective of the response of conjugate hemispheres. Investigation of Van Allen Probes data demonstrates a notable preference of oxygen outflows from the nightside subauroral zone from the sunlit hemisphere. This characteristic eliminates the possibility of nightside auroral precipitation playing a significant role, as it is more prominent in darkness. Instead, it highlights sunlight‐induced ionization as the mass source and enhanced plasma waves from the magnetotail as the energy source. The results presented here further support the nightside subauroral zone as an independent source of magnetospheric oxygen ions. Plain Language Summary: Single‐charged oxygen ions, believed to ultimately originate from the ionosphere, are the main carriers of the ring current during severe space weather, including super geomagnetic storms and substorms. Therefore, comprehending where and how they come from is crucial for understanding the magnetosphere and space weather. Recent studies have reported the nightside subauroral zone as a source region, besides the usually cited cusp, polar cap, and auroral oval. However, the detailed mechanisms for the subauroral oxygen outflows remain open questions. In this study, we address this issue by studying how opposite hemispheres react simultaneously in subauroral oxygen outflow events observed by the Van Allen Probes. Data analysis reveals that these outflows tend to occur in the local summer hemisphere, where the nightside subauroral ionosphere receives more sunlight compared to the opposite hemisphere. This feature rules out nightside auroral precipitation playing a significant role, as it is more noticeable in the dark. Instead, it points to sunlight‐induced ionization as the source of mass and enhanced plasma waves from the magnetotail as the source of energy. Our findings reinforce the idea that the nightside subauroral zone is an important source of ionospheric oxygen outflows. Key Points: The Van Allen Probes have observed oxygen outflows from the nightside subauroral ionosphere in a single hemisphereStatistics reveal a preference for the outflows in sunlit hemisphere, distinguishing them from auroral outflows more prominent in darknessThis preference highlights sunlight‐induced ionization and waves from the magnetotail as the source of mass and energy [ABSTRACT FROM AUTHOR]

The ETH-Library's Image Archive has been using the knowledge of volunteers for over 15 years to identify photographs (aerial images, documentary and scientific photographs, press photography, etc.) correct and improve image metadata or georeference images. The first big challenge in starting so-called crowdsourcing projects is how to find people who might be enthusiastic about such a project. The second big challenge is how to maintain a community and keep it going over a longer period. A variety of experiences, tips as well as do's and don'ts are discussed in the article. [ABSTRACT FROM AUTHOR]

The jet impingement flash technology represents a paramount research subject in the domain of heat and mass transfer. To augment its commercial potential, the conjunction of annular multi‐aperture jet impingement with negative pressure flash evaporation is introduced in this study. The employment of an annular nozzle array is integral to the enhancement of the heat and mass transfer efficiency between the phases. The Realizable k‐ε model is used in this study. The negative pressure flash vaporization model was also developed by introducing a mass source term and an energy source term based on the Mixture model. The flow characteristics are characterized using numerical simulation. Additionally, the λ2 vortex identification criterion is investigated the vortex structure. The simulation results exhibit good agreement with experimental findings, demonstrating that a higher initial vacuum leads to a stronger flashing effect and a more chaotic movement of the flow group within the flow field. Thus, this study provides a reference method for the structural design and optimization of annular symmetric jet impingement negative pressure deammonia chemical equipment for engineering applications. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to describe the fixation location and the time of the longer fixation of expert and novice futsal coaches before the ball was in play in futsal set pieces. Methods: A total of 10 experts (ages 48 ± 5) and 10 novice coaches (ages 40 ± 7) participated in the study. They observed that 38 video clips were created to mimic the attack and defensive set-piece moments of the game. Data were collected in a standardized video analysis task using the pupil invisible eye tracker and processed through the pupil cloud platform. The Mann-Whitney test was conducted to evaluate differences in gaze duration between game moments (attack and defense set pieces) and groups (expert vs. novice). Gaze duration was also compared for gaze location between groups. For further comparisons, the game moments (attack and defense set pieces) and the gaze location were summarized in two-dimensional graphics using correspondence analysis. Results and discussion: The results revealed higher values of gaze duration for attack and defense set pieces for the group of experts than for novices. When considering gaze duration, expert coaches had higher values than novices for the attacker 3, defender 3, barrier 1st, and barrier 2nd gaze locations. The correspondence analysis showed different strategies of visual search and, consequently, gaze locations for attack and defense set pieces. In particular, there was different correspondence for free kicks between the level of expertise and gaze location, while corner and sideline kicks revealed some correspondence between the groups and the gaze location. In free kicks, coaches should be particularly concerned about the relationship between attacker and defender three and the barrier 1st and 2nd line positions. In corner and sideline kicks, coaches should be particularly aware of the relationship between attackers' and defenders' positions. [ABSTRACT FROM AUTHOR]

Ancient calligraphy manuscripts often suffered from degradation due to human factors and natural weathering. Image restoration techniques restore the degraded contents in calligraphy character images to help text-related research and have great significance for cultural heritage protection. Traditional manual calligraphy restoration relies heavily on professional skills and identifying experts for this process is time-consuming. Automatic digital methods do not usually require experts, which have an unsatisfactory performance for severely degraded calligraphy works restoration. In this paper, we first propose a crowdsourcing-based method to identify experts by a novel character similarity inference (CSI) algorithm to restore severely degraded calligraphy characters. Then, we develop LanT, a system to implement crowdsourcing tasks of restoration. We performed an evaluation on restoring a famous Chinese calligraphy work. The experimental results show that CSI can reliably identify experts. Additionally, we evaluate the usability of LanT and the generalization of CSI for Latin calligraphy character restoration. [ABSTRACT FROM AUTHOR]

Researchers studying communication platforms in organizations (i.e., enterprise social media (ESM)) primarily focus on the implications of these technologies for knowledge sharing. In privileging task-based communication we risk overlooking the emotional aspects of communication inherent in worker interactions. This study investigates employees’ communication on ESM in two aviation companies. Interviews with employees (N = 39) revealed that they perceived ESM communication as mainly negative, focusing on venting and accusations, and that employees differed in their likelihood of expressing consonant or opposing emotions. Additionally, moderators’ presence on the ESM at one organization did not result in fewer emotional displays than on the unmoderated ESM. This research highlights the role of visibility in emotional workplace communication and in emotion cycles among organizational members. [ABSTRACT FROM AUTHOR]

AbstractThe advent of web-based interactive technologies has opened up new possibilities for virtual gatherings in 3D environments. Live-streaming, in particular, has gained increasing attention due to its effectiveness in engaging a large number of users in collective online activities. With an emphasis on audience participation, live-streaming shares common characteristics of the outlook of the metaverse and is driving new waves of interaction in virtual gatherings, such as engaging users through crowdsourcing control. However, this type of social interaction has not been examined in the Asian context, and it lacks systematic investigation of user experience with different crowdsourcing control methods. In this paper, we present a novel crowdsourcing control method based on DanMu, the subtitle system of Bilibili, one of the most successful and prevalent live-streaming platforms in Asia. We organized virtual gatherings by live-streaming a Minecraft virtual campus and examined the use of DanMu for crowdsourcing control. Our first study investigated the influence of three crowdsourcing control methods (First Come First Served, Vote, and Super Command) on collective navigation task efficiency and user experience. These influences were further discussed with user activeness and group sizes in a follow-up study. The results showed that Super Command, a representative mode on top of the democratic voting mechanism, offers better user experiences and social richness in large groups. Participants also rated its usability higher in small groups. Besides, virtual gathering in small groups allows greater pragmatic quality, usability, and a sense of agency than in large groups. Our work provides design guidelines for developers and HCI practitioners to develop crowdsourcing control methods and improve novel virtual gathering experiences in virtual worlds and the future metaverse. [ABSTRACT FROM AUTHOR]

The EU aims to drive the data and platform economy by establishing a single European Data Space(s) vision, envisioning it as a catalyst for innovation, novel services, enhanced competition and consumer welfare. It involves implementing existing and forthcoming horizontal and sectorial data-sharing laws to overcome technical, legal, and ethical barriers to (personal and non-personal) data access and achieve specific data-driven policy targets. The initial spaces will cover nine economic sectors, few established and others in development. The study assesses the possible oversight of competition law collusion concerns in these initiatives, using the example of the EU Mobility Data Space (EMDS). By employing competition law qualitative doctrinal methods, it emphasizes the risks of digital collusion within these EU-wide open data frameworks, and it indicates that they could severely harm one or several parameters of competition. It concludes by offering policy recommendations to prevent or minimize collusion risks for the EMDS and suggests further research in other sectors. [ABSTRACT FROM AUTHOR]

Vehicle‐based mobile observations are taken across the world every day by operational and research meteorological organizations, public transportation agencies, and private car manufacturers. Whether directly weather‐related (e.g., air temperature) or not (e.g., wiper speed), the coverage and frequency of these observations holds the promise of filling in gaps between fixed observing stations and greatly improving situational awareness and weather forecasting, from road surface condition‐specific applications and winter road maintenance to urban and street‐level numerical weather prediction and beyond. However, in order to take advantage of these observations, the weather, water, and climate enterprise must work together with the transportation enterprise across academic, public, and private sectors to provide a mechanism for obtaining these data, so that the benefits of using these unconventional observations may be realized. [ABSTRACT FROM AUTHOR]

Crowdsourcing applications that integrate storytelling and geotagging capabilities offer new avenues for engaging the public in cultural heritage. However, standardised evaluation frameworks are lacking. This paper presents an applied evaluation methodology involving the analysis of relevant web-based tools. Towards this goal, this paper presents the development of crowdsourcing applications using, as a case study, the collection of myths and legends associated with the monumental heritage site of the Palace of the Grand Master of the Knights of Rhodes in Greece. Additionally, the paper presents an evaluation conducted through a criteria-based approach and user-based survey. The study reviews the concepts of crowdsourcing and digital storytelling within digital heritage, along with current concepts of living heritage and folklore, and examines relevant initiatives. The evaluation follows a four-stage methodology: (i) initial web-based tool selection based on the minimum requirements of web compatibility, crowdsourced data display, and map-based storytelling capability; (ii) application development using the selected web-based tools; (iii) a five-criteria assessment, based on the factors of open access, usability/tool support, participatory content/story creation, metrics provision and metadata model usage; and (iv) a crowd-based survey, indicating the most effective option. Findings from 100 respondents reveal limited exposure to participatory storytelling applications but interest in contributing content. Social media and influential figures serve as key channels for promoting crowdsourcing open calls. The results highlight gaps in understanding user expectations and perceptions, suggesting future research for gaining insights into engagement rates. [ABSTRACT FROM AUTHOR]

Understanding species composition across temporal and spatial scales through participatory monitoring has contributed to the development of several studies focused on biodiversity in Neotropical ecosystems. Habitat loss and the illegal collection of bromeliads pose significant threats to bromeligenous frogs, which depend on the rainwater collected between bromeliad leaves for egg and tadpole development. In this study, we compiled a comprehensive dataset of bromeligenous frogs using data from "Projeto Bromélias" on the iNaturalist citizen science platform. Our dataset includes records of 85 species of bromeligenous frogs, representing 52% of the 164 known species that reproduce in bromeliads. These species belong to 33 genera and 10 families and are reported from 18 countries. Twenty-eight species are listed in threatened categories on a global scale. Our findings extended the known geographic distribution of four species. Notably, the green and black poison dart Frog, Dendrobates auratus, was recorded as a non-native species in Hawaii, USA. Regarding the temporal data, the number of bromeligenous records increased substantially after 2009. This study highlights the value of citizen science platforms as important tools for monitoring bromeliad inhabitants and contributing to management and conservation initiatives. [ABSTRACT FROM AUTHOR]

ARIADNE (Axion Resonant InterAction Detection Experiment) is a table-top experiment that intends to search for QCD axions from exotic spin-dependent interactions mediated by axion between nuclei at sub-mm range. This experiment includes a non-magnetic mass to source the axion field, and a dense ensemble of hyper-polarized 3He nuclei to detect the axion field with nuclear-magnetic-resonance (NMR)-based method. The expected NMR signal from the interaction could be easily buried in the noise spectrum of the magnetometer, especially in a frequency range (~ 100 Hz) where the interaction signal is supposed to exist. In this work, we report optimization of SQUID gradiometer for ARIADNE including noise spectrum measurement. [ABSTRACT FROM AUTHOR]

This paper shows how the leading Higher Education Institutions (HEIs) in the UI GreenMetric World University Ranking communicate their environmental sustainability policies through their websites. Specifically, this paper analyses the presence of sustainability‐related sustainable development goals on the websites of the top 100 universities in the UI GreenMetric World University Ranking (2021). In the area of university social responsibility, sustainability is less commonly communicated, hence the need to delve deeper into the presence of these messages, carrying out a descriptive study using content analysis as a form of data extraction. The conclusion is that neither position in the ranking nor geographic area are decisive for developing better communication about sustainability. In terms of content, sustainability features on the websites are in three key dimensions of the communication of HEIs worldwide (Climate change, Efficiency of sustainable cities and communities and Striving for peace and justice). Thus, the areas for improvement are around the positioning of this sustainability‐related content on their home pages, improving location and format (especially video). Moreover, reinforcing reputational messages, through plans or programmes, as well as improving engagement working on the multi‐directionality to other social media, is required. [ABSTRACT FROM AUTHOR]

More than 5 billion people in the world own a smartphone. More than half of these have been used to collect and process health-related data. As such, the existing volume of potentially exploitable health data is unprecedentedly large and growing rapidly. Mobile health applications (apps) on smartphones are some of the worst offenders and are increasingly being used for gathering and exchanging significant amounts of personal health data from the public. This data is often utilized for health research purposes and for algorithm training. While there are advantages to utilizing this data for expanding health knowledge, there are associated risks for the users of these apps, such as privacy concerns and the protection of their data. Consequently, gaining a deeper comprehension of how apps collect and crowdsource data is crucial. To explore how apps are crowdsourcing data and to identify potential ethical, legal, and social issues (ELSI), we conducted an examination of the Apple App Store and the Google Play Store in North America and Europe to identify apps that could potentially gather health data through crowdsourcing. Subsequently, we analyzed their privacy policies, terms of use, and other related documentation to gain insights into the utilization of users' data and the possibility of repurposing it for research or algorithm training purposes. More specifically, we reviewed privacy policies to identify clauses pertaining to the following key categories: research, data sharing, privacy/confidentiality, commercialization, and return of findings. Based on the results of these app search, we developed an App Atlas that presents apps which crowdsource data for research or algorithm training. We identified 46 apps available in the European and Canadian markets that either openly crowdsource health data for research or algorithm training or retain the legal or technical capability to do so. This app search showed an overall lack of consistency and transparency in privacy policies that poses challenges to user comprehensibility, trust, and informed consent. A significant proportion of applications presented contradictions or exhibited considerable ambiguity. For instance, the vast majority of privacy policies in the App Atlas contain ambiguous or contradictory language regarding the sharing of users' data with third parties. This raises a number of ethico-legal concerns which will require further academic and policy attention to ensure a balance between protecting individual interests and maximizing the scientific utility of crowdsourced data. This article represents a key first step in better understanding these concerns and bringing attention to this important issue. [ABSTRACT FROM AUTHOR]

Nanospray desorption electrospray ionization (nano‐DESI) is an ambient ionization mass spectrometry imaging (MSI) approach that enables spatial mapping of biological and environmental samples with high spatial resolution and throughput. Because nano‐DESI has not yet been commercialized, researchers develop their own sources and interface them with different commercial mass spectrometers. Previously, several protocols focusing on the fabrication of nano‐DESI probes have been reported. In this tutorial, we discuss different hardware requirements for coupling the nano‐DESI source to commercial mass spectrometers, such as the safety interlock, inlet extension, and contact closure. In addition, we describe the structure of our custom software for controlling the nano‐DESI MSI platform and provide detailed instructions for its usage. With this tutorial, interested researchers should be able to implement nano‐DESI experiments in their labs. [ABSTRACT FROM AUTHOR]

The inhomogeneous Log-Gaussian Cox Process (LGCP) defines a flexible point process model for the analysis of spatial point patterns featuring inhomogeneity/spatial trend and aggregation patterns. To fit an LGCP model to spatial point pattern data and study the spatial trend, one could link the intensity function with continuous spatial covariates. Although non-continuous covariates are becoming more common in practice, the existing estimation methods so far only cover covariates in continuous form. As a consequence, to implement such methods, the non-continuous covariates are replaced by the continuous ones by applying some transformation techniques, which are many times problematic. In this paper, we develop a technique for inhomogeneous LGCP involving non-continuous covariates, termed piecewise constant covariates. The method does not require covariates transformation and likelihood approximation, resulting in an estimation technique equivalent to the one for generalized linear models. We apply our method for modeling COVID-19 transmission risk in East Java, Indonesia, which involves five piecewise constant covariates representing population density and sources of crowd. We outline that population density and industry density are significant covariates affecting the COVID-19 transmission risk in East Java. [ABSTRACT FROM AUTHOR]

Crowdsourcing has long been an effective way for data collection. Despite numerous research efforts devoted to crowdsourcing security, most crowdsourcing platforms in practice are still storing and processing plaintext data, which is vulnerable to in-house attacks. To fill this gap, we envision a full-fledged crowdsourcing platform with end-to-end encryption (E2EE), where the crowdsourced data remains always encrypted to the platform. Such a design would serve as an in-depth defense strategy against data breaches from both internal and external threats and provide technical means for crowdsourcing service providers to meet various stringent regulatory compliance. To realize this vision, we will discuss the technical requirements and related challenges, including: (1) securing worker profiles in task matching to respect individual privacy, (2) assuring high-quality crowdsourced data to enhance data values, (3) enabling versatile data analytics to uncover data insights, (4) protecting data at the front-end to fully achieve E2EE, and (5) preventing the abuse of E2EE for practical deployment. We will overview the limitations of prior arts in meeting all these requirements and identify potential research directions for the roadmap ahead. [ABSTRACT FROM AUTHOR]

Online participant recruitment ("crowdsourcing") platforms are increasingly being used for research studies. While such platforms can rapidly provide access to large samples, there are concomitant concerns around data quality. Researchers have studied and demonstrated means to reduce the prevalence of low‐quality data from crowdsourcing platforms, but approaches to doing so often involve rejecting work and/or denying payment to participants, which can pose ethical dilemmas. We write this essay as an associate professor and two institutional review board (IRB) directors to provide a perspective on the competing interests of participants/workers and researchers and to propose a checklist of steps that we believe may support workers' agency on the platform and lessen instances of unfair consequences to them while enabling researchers to definitively reject lower‐quality work that might otherwise reduce the likelihood of their studies producing true results. We encourage further, explicit discussion of these issues among academics and among IRBs. [ABSTRACT FROM AUTHOR]

E-diasporas are communities of diaspora members utilizing digital technologies and data platforms to establish connections among themselves and with their homelands. In response to the pandemic, governments worldwide have intensified efforts to reinforce e-diasporas. However, these endeavours often rely on social media extractivist Big Tech platforms, which are referred to as 'hyperconnected diasporas' in this article. This trend potentially poses a threat to institutional trust and data privacy. This article introduces HanHemen (ThereHere in Basque language, Euskera), an ongoing action research-driven e-diaspora platform facilitated by the Basque Government. HanHemen aims to ensure data privacy through experimentation with blockchain technologies and co-production with end-users via DAOs. This article reveals findings from an online survey completed by 419 Basque diasporic citizens (N = 1,385), who demonstrated support for digital nomadism (62%) and expressed concerns about data privacy (84%). These findings highlight the need to overcome the mainstream 'hyperconnected diaspora' trend through HanHemen. [ABSTRACT FROM AUTHOR]

This study addresses the pervasive challenges of low hepatitis B (HBV) and hepatitis C (HCV) testing rates coupled with the stigma associated with these diseases in low‐ and middle‐income countries (LMICs) with a special focus on Bangladesh. This study aims to introduce an innovative crowdsourcing intervention that involves medical students, a crucial cohort with the potential to shape healthcare attitudes. Through a structured crowdsourcing approach, the study designs and implements a digital intervention to counter stigma and promote testing among medical students in Dhaka, Bangladesh. Participants submitted brief videos or texts aiming to encourage hepatitis testing and reduce stigma. The call, advertised through meetings, emails, and social media, welcomed entries in English or Bengali over 3 weeks. A panel of six judges evaluated each entry based on clarity, impact potential, innovation, feasibility, and sustainability, awarding prizes to students behind the highest‐rated submissions. Seventeen videos and four text messages received an average score of 5.5 among 440 surveyed medical students, predominantly 22 years old (16%) and in their fourth year (21%). After viewing, 360 students underwent screening, identifying two previously undiagnosed HBV cases referred for care; no HCV infections were found. Notably, 41% expressed concerns about individuals with HBV working in hospitals or having a doctor living with HBV. In conclusion, this pilot showcases the power of medical students in spearheading campaigns to counter hepatitis stigma and encourage testing. By utilizing crowdsourcing, the study introduces an innovative approach to a persistent issue in LMICs specially in Bangladesh, offering a model that could potentially be adapted by other regions grappling with similar challenges. [ABSTRACT FROM AUTHOR]