Gender and Sport: An Analysis of Gender Specific Language in Basketball Commentaries
Bibliographic record
Abstract
Summary. The aim of present paper is to compare the language used in men and women basketball commentaries, and to discuss the main influential factors for these differences to occur. Firstly, two basketball matches (women and men gold final games in London Olympics 2012) are chosen for the analysis. The first quarter of men’s game and the first quarter of women’s game are transcribed, and the data is analyzed in several aspects, which are presented below. Secondly, the discussion is referred to books and articles presenting researches on language, gender, and sports. \n\tIn the theoretical part, the discussion is carried out along the topics on women involvement in sports, comparison of men and women physical bodies, gender-based occupational distribution, genders specific language in televised sports and basketball commentaries, the coverage of women’s sport in mass media, and gender specific language used by media channels. This part also argues the stereotypical point of view that still prevails in the society for acceptable and unacceptable behaviors determined by gender. \n\tIn the practical part, the transcribed data is presented for the analysis in three categories: the use of statistics, the interpretation of physical contact, and gender specific descriptions and references. The discussion contains graphics, tables with finding, and relevant examples from the men and women basketball matches. The findings are discussed referring to researches carried out by scholars... [to full text]
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".