Are layoffs an industry norm? Exploring how industry‐level job decline or growth impacts firm‐level layoff implementation
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 Corporate layoffs are a globally prolific organisational activity, but little is known about how industry‐level employment loss or gain impacts firm‐level layoff implementation. Grounded in institutional theory, this study posits that firms in industries experiencing employment decline align with a cost‐containment approach, while firms in industries experiencing employment growth focus on social exchange theory when executing employee layoffs. Analysis of 573 mass layoffs from March 2013 to May 2019 compared downsizing scope (layoff severity and frequency), explanations, alternatives, advance notice, and firm characteristics (unionisation and firm size) in employment gain versus loss industries. The findings indicate that meaningful differences exist. Firms operating in employment loss industries implement layoffs focused on cost‐containment, including less severe layoffs, less extensive but more demand‐decline focused explanations, and use more cost‐reduction layoff alternatives, when compared to layoffs in employment gaining industries. Firms operating in industries experiencing growth execute layoffs in a manner that maintains the social exchange expectations between employee‐employer. In addition, firms in declining industries are more likely to be unionised and larger than firms in growing industries. This research helps reconcile divergent layoff perspectives by considering how variations in external factors impact corporate layoffs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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