We need to talk about manels: the problem of implicit gender bias in sport and exercise medicine
Bibliographic record
Abstract
In 2015, a website (www.allmalepanels.tumblr. com/) began documenting instances of all-male panels (colloquially known as a ‘manel’). This, along with the Twitter hashtag #manel, has helped drive recognition of the persistent and pervasive gender bias in the composition of experts assembled to present at conferences and other events. Recent social media discussions have similarly highlighted the prevalence of all-male panels in Sport and Exercise Medicine (SEM). While, to our knowledge, all-male panel trends in SEM have not yet formally been documented or published, one need look no further than SEM conference committees, keynote speaker lists, panels and other events to see that it exists in practice. Why, in 2018, is SEM and its related disciplines still failing to identify and acknowledge the role that implicit bias plays in the very structure of our own research, practice and education? SEM is, after all, a profession that contains experts, and serves populations, of all genders. This editorial will introduce the definition, implications and manifestations of implicit gender bias and then explore how the SEM community can begin to address this issue, advance the discussion and develop a more equitable global community.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.017 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".