Media narratives of industrial plant closures in Ontario, Canada, from 2000 to 2019
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it