Gestió de la diversitat a les organitzacions esportives. Un acostament crític
Bibliographic record
Abstract
Mentre que les organitzacions publiques i comercials dels Estats Units, el Canada, Australia, Gran Bretanya i, de forma creixent, les de la Unio Europea, han incorporat el concepte de “gestio de la diversitat”, les organitzacions esportives, ben al contrari, han prestat poca atencio a aquest tema. De fet, s’han resistit a intentar de millorar la representacio de la dona i de les minories racials i etniques. Aquest article examina la idea de “gestio de la diversitat” a la llum de la creixent critica als seus objectius, que es fa evident tant a la literatura academica com a la del sector de la gestio. La bibliografia relacionada amb l’esport, encara que escassa, es exageradament positiva pel que fa al concepte de gestio de la diversitat, i anima les organitzacions esportives a plantejar-se els beneficis de la gestio de la diversitat, pero fent cas omis de la important critica de que aquesta esta sent objecte en l’actualitat. La gestio de la diversitat, tal com s’enten avui dia, contribuira poc a la millora de la representacio de la dona en els llocs de presa de decisions i de lideratge dintre de les organitzacions esportives (el mateix es podria dir sobre la raca/etnia i d’altres grups socials).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.045 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".