You are the Weakest Link, Goodbye (to Physical Inactivity!): A Comment on Irwin et al.
Bibliographic record
Abstract
Over the past two decades there has been a steady growth of research examining the capacity of groups as a means of facilitating improved physical activity behaviors among individuals. These studies have involved a diverse range of populations including young children, older adults, pre- and post natal women, ethnic minorities, to name but a few [1]. The basis for this research stems from the pioneering work of those such as Lewin [2], Cartwright and Zander [3], and, more recently, scholars such as Carron and Spink [4]—who all suggested that an individual is invariably changed when s/he joins a group. The study by Irwin and colleagues [5] provides an interesting and unique example of how group dynamics principles can be harnessed to influence individual behavior, in this case in relation to improved motivation during aerobic exercise. The use of a conjunctive task facilitated by the implementation of a group goal (i.e., team score) is an elegant approach to understanding individual motivation in a co-active exercise setting. These laboratory-based findings align with the outcomes of a number of other studies that have used group-dynamics-based approaches in applied settings [1] and provide fodder for a number of potential future studies to expand upon and explain these findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.060 | 0.097 |
| Insufficient payload (model declined to judge) | 0.019 | 0.018 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".