Portrayals of disability in Canadian newspapers : an exploration of Terry Fox
Bibliographic record
Abstract
Despite Terry Fox's fame and contributions to Canadian history and society, he has rarely been the subject of academic research, and no one thus far has studied how Fox fits into the larger picture of disability awareness in Canada.This qualitative research study is a response to these issues, examining the online archives of newspaper afticles about Fox from the early i980s as viewed through the lenses of the Charity, Medical, and Social, Models of Disability, and traditional stereotypes of persons with disabilities in the media.This research also examines social capital as an influence in newspaper articles, in addition to viewing the articles as narratives.Findings from this study suggest that the majority of newspaper journalists viewed Fox through the lenses of the Charity and Medical Models of Disability, as a dying cancer patient attempting to raise funds for a cure-not as a disabled Canadian.This research also shows that Fox was portrayed by journalists in several traditionally stereotyped ways, but also in one new way: as a folk hero.As well, this research reveals that Fox's narrative was the most detailed in the articles and photographs that appeared at the onset of his terminal illness and until his death.Finally, this research demonstrates that social capital played a strong role in Fox's fame, in his ability to secure donations, and in the way his story was immortalized after his death.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.041 | 0.011 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".