Exploring the Concussion Experience Within Sport: An Authoethnographic Study
Bibliographic record
Abstract
Concussions are a highly individualized experience, with different profiles of expression \nencapsulating a diverse range of symptom sequalae. However, the lived through experience of those who have sustained a concussion oftentimes takes a backseat to the more standardized quantitative medical approach to healing. The purpose of this thesis is to engage and address gaps in literature and document the necessity and benefit of qualitative research to understand the nuances of the concussion experience by utilizing an autoethnographic approach and a Critical Disability Studies (CDS) method of writing termed “Disability Life Writing.” Additionally, this thesis attempts to remove a barrier to concussion information by presenting concussion knowledge in accessible terminology and language, aiming to make concussion awareness available to those without knowledge of medical terminology or discourse. Regarding concussions in sport, this thesis aims to illuminate hidden values and ideologies within a sporting culture that ultimately work to socialize an athlete to play through pain and hide/not disclose injuries such as a concussion to peers, coaches, or other members of the sporting culture. The author analyzed all the aims listed above through a CDS lens using core CDS concepts such as stigma, stereotyping, normalcy, and invisible disabilities as analytic touchstones.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".