Textually mediated discourses in Canadian news stories: Situating nurses’ salaries as the problem
Bibliographic record
Abstract
The aim of this article is to elucidate how nurses are positioned in Canadian news stories regarding their salaries. While the image of nursing in mass media has been widely studied, few studies explore how nurses are constructed in news stories. Drawing on ideas from institutional ethnography together with discourse analysis, this discussion highlights public textual discourses about nurses' salaries in Canadian news stories. The media discourse was found to distort the issues by focusing attention on nurses. Recognizing how these textual distortions mediate and construct messages is important in understanding how nurses and their work are constructed in the media. This discussion seeks to inform readers about how nurses are situated within commonly circulated discourses in the media. It also seeks to contribute to the literature about the nurse's image and how nurses and their work are portrayed in the public realm. It concludes by recommending increased awareness about how nurses are talked about in mass communication and the need to disrupt these messages and their underlying assumptions.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.038 | 0.034 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".