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Background: Influence is at the very core of physical education and sport pedagogy. Indeed, a large and growing body of work has focused on the (inter)actions of sport pedagogues which "are" deemed to be influential in terms of shaping the thoughts, feelings and actions of others. In comparison, little attention has been paid to the practices of sport pedagogues that are "non"influential or unintentionally influential. That is, when pedagogues or learners choose "not to" do something, how they are "not" influenced/influential, or when practice (unintentionally) influences those who were (or were not) the original target. Paying greater attention to these issues holds strong potential to develop more critical and ethical understandings of influence that can inform the education and development of sport pedagogues. Aims: The aims of this study are two-fold. Firstly, we seek to break new ground by providing novel insights into "how, when, why, for whom," and "under which circumstances" pedagogical (inter)action is "not" influential, and where (inter)actions have had an unintended influence. Secondly, and relatedly, we seek to advance and illustrate methodological perspectives capable of critically understanding this topic. Data collection: Data were generated using a bricolage of methods (i.e. participant observation, semi-structured interviews, and stimulated recall interviews) as part of a critical realist ethnography with one representative-level junior cricket squad in the UK. Data were subject to emic and etic readings in response to the aims of the study. In total, 182 h of observational data and 46 h of interview data were generated and analysed using the Critical Incident Technique (CIT). Here, the primary sense making devices were provided by Jones and Wallace's (2005) theorising of orchestration, Elder-Vass' (2010) causal power of social structures, and Mason's (2002) concept of noticing. Analysis and discussion: A small number of richly detailed and critical coaching (inter)actions are presented to illustrate the emergent meaning-making of different tacticians and targets of (non)influence. Specifically, the analysis introduces incidents that are illustrative of three novel types of (non)influence. The examples all highlight pathos between the coaches' original intentions for (and reading of) influence, and the "actual" influence of practice for the athlete(s). Here, the discussion provides accounts of: (1) noninfluence on targeted individuals; (2) unintended influence on non-targeted individuals; and (3) unintended influence on targeted individuals. Conclusion: Overall, this paper contributes to a growing body of critical pedagogical research, positioning the work of influence less obtrusively. It provides a novel methodological, theoretical, and empirical contribution which practitioners, educators, researchers, and other stakeholders can engage with to critically consider "how, when, why, for whom," and "under which circumstances" (inter)actions are (likely to be) influential or not. Further work could examine what pedagogues and learners decide to do "and" not do, as well as what they notice "and" do not notice as a basis to develop more critical and ethical practices.

Teaching chemistry without access to a traditional laboratory space is an ongoing challenge that has become especially relevant because of the SARS-CoV-2 pandemic. While several remote learning options exist for covering general chemistry concepts (including kitchen-based experiments, online modules, and virtual reality), few options provide opportunities for hands-on learning about the chemistry of synthetic polymer materials. Here, we offer remote learning modules that use household adhesives as a platform for teaching polymer chemistry outside of the laboratory. These modules are designed for students who have taken at least one semester of organic chemistry and have varied hands-on time commitments, ranging from 2 to 10 total hours each. Concepts covered include polymer synthesis, intermolecular interactions, thermomechanical properties, structure-function relationships, and molecular design. The experiments described in these modules also give students a chance to practice research-relevant skills such as searching for primary literature sources, fabricating test samples, explaining unexpected experimental results, and revising experimental procedures to improve methodologies. Ultimately, these modules provide educators with an additional tool for teaching experimental chemistry outside of the laboratory.

This study differentiated the kinematics of the beach flags sprint start between five elite (three males, two females; age = 21.2 ± 2.6 years; height = 1.71 ± 0.04 m; mass = 66.2 ± 5.9 kg) and five non-elite (three males, two females; age = 20.4 ± 1.7 years; height = 1.69 ± 0.08 meters [m]; mass = 61.6 ± 5.7 kilograms) sprinters. A high-speed camera filmed the start. Timing gates recorded the 0-2, 0-5, and 0-20 m intervals. Data included body position during the start and at take-off; start time; first step length; and sprint times. A Mann-Whitney U-test determined significant (p < 0.05) between-group differences; effect sizes (ES) were also calculated. Elite sprinters had a greater take-off trajectory angle (p = 0.01; ES = 2.57), and were faster over the 0-2 (p = 0.02; ES = 1.77), 0-5 (p = 0.05; ES = 1.20), and 0-20 m (p = 0.02; ES = 1.83) intervals. Large effects were found for: greater take-off swing leg hip flexion (ES = 1.13) and trunk lean (ES = 1.37); longer duration start time (ES = 1.33); and longer first step length (ES = 1.23) in elite sprinters. A longer start time assists with force generation, which in conjunction with increased hip flexion, could translate to a longer first step. Increased trunk lean shifts the take-off trajectory angle towards the horizontal. A greater trajectory angle at start take-off, which could be advantageous for force production during sprint performance, is likely necessary for beach flags. [ABSTRACT FROM AUTHOR]

We previously developed a model, based on the precepts of optimal patch use, to compare habitat quality both within and between environments. Here we illustrate the use of this model in a study estimating quality of winter habitats (deer yards) of white-tailed deer Odocoileus virginianus near the northern limit of their range by following their foraging behaviour. We compare giving up densities (GUDs), the amount of food remaining in a patch when a forager ceases foraging there, with and without the presence of supplemental food in order to draw inferences about the relative quality either of habitats within an environment or of distinct environments. We use our model to evaluate the impact of alterations to the winter habitat of deer at two distinct sites that differed in their level of predation risk and food availability. The first site, the Mont Rigaud deer yard, was surrounded by farm land and gradually-expanding suburbs. Predators were rare and food was available in winter either in farm fields or around private homes but deer browsing in the past had left little food in the forest. At the second site, the Calumet deer yard, deer experienced a higher predation risk and did not have access to supplemental food from farm fields or private homes. However, past browsing by deer had not drastically reduced food in the forest. We offered food to deer in four habitats per site (forest, forest edge, clearing, clearing edge) with four to six replicates per site and measured the GUDs after 24 h. Analysis of these data, interpreted according to our model, suggests that deer are more sensitive to metabolic costs at Mont Rigaud and food availability at Calumet; predation risk does not alter deer behaviour between the two sites. Within habitats, deer at Mont Rigaud reacted to clearings as though they imposed higher metabolic costs than the forest. They also reacted to an interaction in which missed food costs influenced GUD only when metabolic costs were not too high. Thus our model appears to provide a convenient tool for comparing habitat quality both within and between environments. [ABSTRACT FROM AUTHOR]

Investigates the relative effectiveness and joint effects of three selected reinforcements in a repetitive task situation. Use of incentive-pay rates and partially reinforced bonus; Problems with early pay experiments; Dysfunctional behavior of workers resulting from financial incentives.