Sports equity: a new BJSM e-Edition brings the fundamentals back into focus
Bibliographic record
Abstract
> Change your leaves, keep intact your roots > > –Victor Hugo Throughout history and across cultures, nearly all human societies have constructed systems of privilege and power that oppress, exploit and disadvantage some, while empowering others.1 Across sectors, the powerful have engineered these systems to achieve step-change economic gains at the expense of the oppressed. In addition to wealth, systems of oppression also generate non-material benefits for those on the weighty side of the power imbalance. Consciously or not, as micro-aggressions and macro-aggressions from the advantaged are dealt down the power gradient to the disadvantaged, social advancement opportunities and recognition disproportionately benefit the dominant group.1 The key here is that people actively thought this through . These intentionally designed systems divide society into a dystopian hierarchy based on race/ethnicity, gender, perceived ability, wealth, age and more, despite the fundamental human rights all people are equally due. As clinicians and academics, we have the opportunity to consciously and deliberately expose and dismantle societal biases in our field of Sport and Exercise Medicine (SEM). While …
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.050 | 0.029 |
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".