Is the pen mighter than the podium? An analysis of Canadian online news articles and their portrayals of women on Team Canada during the 2021 Tokyo Paralympic Summer Games
Bibliographic record
Abstract
This thesis examined the portrayal of Women on Team Canada in online Canadian news articles during the 2021 Tokyo Summer Paralympics. The study sought to answer the primary research question: how were women athletes from Team Canada depicted in online Canadian news articles during the event? A qualitative content analysis was conducted using the spiral model, informed by feminist disability studies and the social model of disability. The sample consisted of 51 online news articles, including their text, titles, and photos. Three primary themes emerged: medicalized conceptions of disability, lack of intersectional identities, and reporting easy-to-understand information. It is important to acknowledge that there have been improvements from past coverage, including the text and titles focusing more on women athletes’ athletic skills. Despite these improvements, more work is needed to incorporate social perspectives on disability in reporting, address intersectional identities such as race and sexuality, and include educational information about para-sports and events. This study contributes to the growing body of literature on the representation of Paralympic athletes in media and provides recommendations for more inclusive and equitable coverage in the future.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".