Academic Competition in the School System: At What Cost?
Bibliographic record
Abstract
Competition pervades our culture across sports, entertainment, politics, and corporations, seeping also into educational institutions. Today, children are urged not only to "play to win" but also to "learn to win." Despite awareness of competition's negative psychological and social impacts, it remains a cornerstone of the educational system as it is perceived as a strong motivating factor for academic achievement. However, academic competition has received less attention than its athletic and social counterparts, with previous research often overlooking its effects on interpersonal relationships. Existing studies have either used inappropriate measures for academic settings or failed to differentiate between other-referenced and task-oriented competition, which respectively focus on surpassing peers for status and on personal growth. This thesis introduces new scales tailored for assessing academic competition among adolescents. A pilot study involving 532 adolescents in southwestern Ontario (Mage =15.23) validates these scales through factor analysis using Principal Component Analysis, distinguishing between other-referenced and task-oriented competition. The new scales demonstrate reliability, with Cronbach's alpha coefficients of .789 for other-referenced competitiveness and .825 for task-oriented competitiveness. Regression analyses reveal a significant positive association between other-referenced competitiveness and bullying perpetration, while task-oriented competitiveness shows a moderate inverse relationship with bullying. These findings underscore the need to differentiate between competition for skill development and for status, as the latter may inadvertently foster bullying tendencies. This study emphasizes the importance of nuanced understanding in academic competition and its implications for student well-being. The discussion encompasses implications, limitations, and avenues for future research in this area.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".