Sport Culture and Adversity Explained: Exploring My Experience as a Woman Working in the Sport Industry
Bibliographic record
Abstract
The purpose of this study is to investigate the adversities women face in the sports industry through an autoethnographic style. Through practice-based research, this study explores how sports are constructed by men, for men, and how the marginalization of women, representation of women in sport leadership, mediocrity, and self-limitation practices impact women pursuing careers in the sport industry. To accomplish this objective, I reflect on how my personal experiences as an identifying woman working in sport align with current gender and feminist research that exist in academia. A literature review was written in narrative style and based on various secondary sources, including scholarly journals in sport and gender studies. This study assessed information on the male gaze, descriptive and prescriptive gender stereotyping, role congruity theory, tokenism, emotional labour, and risks of self-promotion. This study examines how the concepts and theories mentioned above are present specifically in sport organizations and sport communities. The findings are demonstrated through qualitative data collected through an autoethnographic approach where I use self-reflection and writing to discuss and examine a woman’s experience in sport. With this study, I produced a media project called “Maiden Mascot”; an animated short film, distributed as a recorded storyboard, focusing on the narrative of women working in sport. My media project is an example of how producing digital storytelling content that shares the experience of a woman working in sport can provide a space for educating audiences about the issues and inspiring societal change. The combination of my paper and project stand to reason a need for future research in analyzing the impact of sharing stories of women working in sports culture through digital media outlets.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".