Bibliographic record
Abstract
Emerald Studies in Sport and Gender promotes research on two important and related areas within sport studies: women and gender.The concept of gender is included in the series title in order to problematise traditional binary thinking that classifies individuals as male or female, rather than looking at the full gender spectrum.In sport contexts, this is a particularly relevant and controversial issue, for example, in the case of transgendered athletes and female athletes with hyperandrogenism.The concept of sport is interpreted broadly to include activities ranging from physical recreation to high-performance sport.The interdisciplinary nature of the series will encompass social and cultural history and philosophy as well as sociological analyses of contemporary issues.Since any analysis of sport and gender has political implications and advocacy applications, learning from history is essential.Contributors to the series are encouraged to develop an intersectional analysis where appropriate, by examining how multiple identities, including gender, sexuality, ethnicity, social class and ability, intersect to shape the sport experiences of women and men who are Indigenous, racialised, members of ethnic minorities, LGBTQ, working class or disabled.We welcome submissions from both early career and more established researchers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.707 | 0.521 |
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".