Framing Layoffs: Media Coverage, Blame Attribution, and Trade-Related Policy Responses
Bibliographic record
Abstract
Abstract Who is blamed when factories close or when there are mass layoffs? Whether it be the closing of an auto plant or the threatened off-shoring of the Carrier furnace factory, media reports frequently incorporate justifications—or frames—that provide context about the closure or layoffs. We conduct an analysis of media coverage of factory layoffs in the United States and Canada, and find that the most common frames often include foreign competition and trade policy, changing market conditions, or exogenous shocks, such as the pandemic. We argue that such frames alter who the public holds responsible and thus affects the public’s preferred policy responses. We test the effect of media frames on the public’s blame attribution and subsequent policy preferences using a survey experiment about General Motors factory closings. The results from a sample of almost 6,000 respondents in the US and Canada show that the public is quick to shift blame to the government, reducing blame to the company, and shifting attention to particular government responses. We find that the most common media frames significantly shift support for trade policy in both countries, but have no effect on domestic public assistance programs such as unemployment benefits or retraining and education programs. Notably, most treatment effects are similar across ideological types. The results hold practical implications in terms of the incentives of politicians to promote specific explanations of factory closings and theoretical implications in terms of moderating the highly partisan expectations within the current literature on economic blame attribution.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".