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Although counts of tweets citing academic papers are used as an informal indicator of interest, little is known about who tweets academic papers and who uses Twitter to find scholarly information. Without knowing this, it is difficult to draw useful conclusions from a publication being frequently tweeted. This study surveyed 1,912 users that have tweeted journal articles to ask about their scholarly-related Twitter uses. Almost half of the respondents (45%) did not work in academia, despite the sample probably being biased towards academics. Twitter was used most by people with a social science or humanities background. People tend to leverage social ties on Twitter to find information rather than searching for relevant tweets. Twitter is used in academia to acquire and share real-time information and to develop connections with others. Motivations for using Twitter vary by discipline, occupation, and employment sector, but not much by gender. These factors also influence the sharing of different types of academic information. This study provides evidence that Twitter plays a significant role in the discovery of scholarly information and cross-disciplinary knowledge spreading. Most importantly, the large numbers of non-academic users support the claims of those using tweet counts as evidence for the non-academic impacts of scholarly research.

This paper explores the association between job satisfaction and gender for workers with disabilities, using data from the Panel Survey of Employment for the Disabled, which interviews officially registered persons with disabilities in Korea. To take full advantage of the longitudinal data, we apply random-effects ordered probit models to investigate the underlying factors that affect gender differentials in job satisfaction. Our findings reveal that merely different work values between women and men do not account for the significantly higher job satisfaction among women. We suggest that workers’ expectations play a role in explaining why female workers are happier in the workplace than their male counterparts; that is, holding other factors constant, women’s expectations from jobs are lower than men’s. This hypothesis ispartiallysupported by the empirical analyses that gender differentials diminish among the highly educated workers, for whom there is less likely to be a gender gap in terms of job expectations.

The occupational profiling system driven by the traditional survey method has some shortcomings such as lag in updating, time consumption and laborious revision. It is necessary to refine and improve the traditional occupational portrait system through dynamic occupational information. Under the circumstances of big data, this paper showed the feasibility of vocational portraits driven by job advertisements with data analysis and processing engineering technicians (DAPET) as an example. First, according to the description of occupation in the Chinese Occupation Classification Grand Dictionary, a text similarity algorithm was used to preliminarily choose recruitment data with high similarity. Second, Convolutional Neural Networks for Sentence Classification (TextCNN) was used to further classify the preliminary corpus to obtain a precise occupational dataset. Third, the specialty and skill were taken as named entities that were automatically extracted by the named entity recognition technology. Finally, putting the extracted entities into the occupational dataset, the occupation characteristics of multiple dimensions were depicted to form a profile of the vocation.

When industrial robots are adopted by firms in a local labor market, some workers are displaced and become unemployed. Other workers that are not directly affected by automation may however fear that these new technologies might replace their working tasks in the future. This fear of a possible future replacement is important because it negatively affects workers’ job satisfaction at present. This paper studies the extent to which automation affects workers’ job satisfaction, and whether this effect differs for high- versus low-skilled workers. The empirical analysis uses microdata for several thousand workers in Norway from the Working Life Barometer survey for the period 2016–2019, combined with information on the introduction of industrial robots in Norway from the International Federation of Robotics. Our identification strategy exploits variation in the pace of introduction of industrial robots in Norwegian regions and industries since 2007 to instrument workers’ fear of replacement. The results indicate that automation in industrial firms in recent years have induced 40% of the workers that are currently in employment to fear that their work might be replaced by a smart machine in the future. Such fear of future replacement does negatively affect workers’ job satisfaction at present. This negative effect is driven by low-skilled workers, which are those carrying out routine-based tasks, and who are therefore more exposed to the risks of automation.

ObjectivesTo describe level of education and work participation among adults with congenital unilateral upper limb deficiency (CUULD) in Norway and to explore associations between work participation and demographic and clinical factors.MethodsCross-sectional study. In 2012, a postal questionnaire was sent to 186 persons with congenital limb deficiency (CLD), age ≥ 20 years, registered at the TRS National Resource Center for Rare Disorders. In the original CLD study, 77 persons with CUULD responded. In this paper 64 persons with CUULD of working age (20-67 years) are included. Data on demographic factors as education level and work participation, and clinical factors including limb deficiency characteristics, chronic pain (Standardized Nordic Questionnaire), fatigue (Fatigue Severity Scale), physical and mental health (SF-36) were analyzed through descriptive and comparable statistics and logistic regression analyses.ResultsSixty-four persons participated, mean age 43.4 (SD 13.7; range 20-67 years), 45 were women. Education level >13 years was reported by 34. Forty- three of the 64 participants were employed, 21 were prematurely retired (disability benefits). 11 of the 43 employed, and 6 of the 21 prematurely retired had completed vocational education. Physically demanding occupations (work activities that required standing, walking and lifting) were reported by 25 of the 43 employed and 13 of the 21 prematurely retired. 17 of the 64 reported need for further adaptions in their workplaces. The strongest predictors of work participation were younger age (OR 0.86) and good physical health (OR 1.21).ConclusionTwo thirds of persons with CUULD were employed; while one third was prematurely retired and had left work earlier than expected. This suggests that persons with CUULD may experience challenges in work participation. Although levels of education were relatively high, several had chosen careers that required physical strain. Younger age and good physical health were the most important factors mediating work participation.

Hearing loss is the most common sensory impairment, but limited studies focused on the association of socioeconomic status (SES) with hearing loss among adults of working age. This paper aimed to fill this gap among Chinese adults. We obtained data from Ear and Hearing Disorder Survey conducted in four provinces of China in 2014-2015. The survey was based on WHO Ear and Hearing Disorders Survey Protocol and 25,860 adults aged 25 to 59 years were selected in this study. Trained local examiners performed pure tone audiometry to screen people with hearing loss, and those who were screened positively for hearing loss were referred to audiologists to make final diagnosis. SES was measured by occupation, education and income. Results show after adjusting for SES measures and covariates, in urban areas, compared with white-collar workers, blue-collar workers and the unemployed were more likely to have hearing loss, with an odds ratio of 1.2 (95%CI: 1.0, 1.3) and 1.2 (95%CI: 1.0, 1.4), respectively. Compared with people with education of senior high school or above, those with junior high school, primary school and illiteracy had 1.6 (95%CI: 1.4, 1.8), 2.1(95%CI: 1.7, 2.5) and 2.6 (95%CI: 1.9, 3.7) times as likely to have hearing loss, respectively. In rural areas, the unemployed had 1.5 (95%CI: 1.0, 2.3) times the risk of hearing loss compared with white-collar workers, and illiterates had 1.6 (95%CI: 1.6, 2.1) times the risk of hearing loss compared with people with education of senior high school or above, after SES variables and covariates were taken into considerations. Income was not significantly associated with hearing loss in urban and rural areas. In conclusion, SES, in the form of occupation and education, was associated with hearing loss among working-aged population, and further studies are needed to explore the mechanism of such association.