Incorrigible While Incarcerated: Topic Modeling Mainstream Canadian News Depictions of Ashley Smith
Bibliographic record
Abstract
Ashley Smith, who is typically presented in the media as mentally ill, was nineteen years old when she died from self-strangulation in an Ontario women's prison on October 19 th , 2007.In this thesis, I explored how Ashley Smith's actions and death were portrayed in four mainstream Canadian newspapers (Globe and Mail, Telegraph-Journal, Toronto Star and National Post).My aim in this thesis is to critically analyze patterns of stereotypes of mental illness present in these news articles and connect these patterns to labeling theories.To accomplish this goal, I used a mixed methods approach that combined computerized topic modeling with critical reading.Topic modeling revealed three variables that affected topic weight: the timing of the news coverage, newspaper political affiliation and circulation/location.These three variables also impacted how these newspapers depicted Ashley Smith as mentally ill, through their use of generic and/or negative terminology, medicalization and vulnerability stereotypes.iii Dedication This thesis is dedicated to the memory of Pat Seguin.A fierce feminist advocate and friend who encouraged me to return to academics and constantly reminded me to never give up without a fight.I would like to take the opportunity to express my deep gratitude to several people for their support during the process of completing this thesis.First, I would like to thank my partner and topic modeling research
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".