Inspiration From The "Biggest Loser": Social Interactions In A Weight Loss Program
Bibliographic record
Abstract
We investigate the role of heterogeneous peer effects in encouraging healthy and sustainable lifestyles. Our analysis revolves around one of the largest and most extensive databases about weight loss, which contains well over 10 million observations that track individual participants' meeting attendance and progress in a large national weight loss program. A few key findings emerge. First, while higher weight loss among average performing peers leads to lower future weight loss for an individual, the effect of the top weight loss performer among peers leads to greater future weight loss for that same individual. Second, the discouraging effects from average peers and encouraging effects from top performing peers are magnified for individuals who struggled with weight loss in the past. Third, the encouraging effect of top performers has a long-run impact on an individual's weight loss success. Finally, we provide suggestive evidence that the discrepancy between the top and average performer effects is not likely an artifact of salience or informativeness of top performers, but instead, driven by its positive impact on the motivation to accomplish weight loss goals. Given our empirical findings, we discuss managerial implications on meeting design.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".