Media narratives of industrial plant closures in Ontario, Canada, from 2000 to 2019
Bibliographic record
Abstract
Abstract Since the 1970s, a defining feature of advanced economies has been industrial plant closures, stemming from the broader process of economic restructuring. Plant closures have been extensively covered by the media due to their adverse effects on localities. However, no media analysis of closures has been conducted in the plant closure literature. In addition to providing a wealth of information, such an analysis can provide insight into media narratives of closures. Media profoundly affects economies by disseminating narratives that influence society, institutions, and politics. To bridge the plant closure and media literature, this paper conducts a media analysis of closures in Ontario, Canada, from 2000 to 2019. Like other advanced economies, the province has experienced many plant closures over the past several decades. The paper found that the overarching narrative presented by the media was that ‘no one is responsible’ for plant closures and therefore ‘no one can or should act’. Also, it was found that differences in media narratives of closures were primarily due to the political slant of news outlets, not city size or scale of news outlets or whether news outlets were independently owned or part of a media conglomerate. Lastly, the paper found that the dissemination of media coverage on plant closures throughout the province was primarily based on the number of job losses, resulting in media coverage of smaller closures remaining localised, while media coverage of larger closures spreading throughout the province.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".