Representations of workers with hearing loss in Canadian newspapers: a thematic analysis
Bibliographic record
Abstract
OBJECTIVE: Participation in the labour force with a hearing impairment presents a number of challenges. This study describes how Canadian newspapers represent workers with hearing loss. DESIGN: Taking a critical framing theory approach, thematic analysis was performed through coding relevant articles, abstracting and hierarchically categorising themes. STUDY SAMPLE: Seven English-language Canadian newspapers were searched for publications between 1995 and 2016. Twenty-six articles met our criteria: discussing paid workers with hearing loss who used English rather than sign language on the job and making reference to workers' competence. RESULTS: We identified a global theme, Focussing on a good worklife or focussing on a limited worklife, composed of three organising themes (1) Prominent individuals struggle, take action, and continue despite hearing loss, (2) Workers with hearing loss in the community create their best day themselves, and (3) Workers with hearing loss, as a generalised whole, are portrayed as either competent or limited. CONCLUSIONS: The dominant framing portrays individual workers as ingenious, determined, and successful. Negative framings were predominantly generalisations to these workers as a group. To generate more positive framings, professionals can build relationships with consumer groups and, when contacted by the media, direct journalists to interview workers with hearing loss.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".