The Influence of the Discursive Power of Unions in the Swift Re-regulation of Slaughterhouse Labour during the COVID-19 Crisis in Germany
Bibliographic record
Abstract
The article analyses the re-regulation of labour in the German meat industry during the COVID-19 crisis. While working and employment conditions have long been criticised with only minor results, the massive coronavirus outbreaks in German slaughterhouses led to a rapid reform of work in the meat industry. We argue that unions were able to exert influence on policy-makers based on the discursive power that they accumulated prior to COVID-19, but that they needed to adapt their framing strategies by including public health concerns to their criticism. That was possible because the outbreaks endangered local residents as well as the slaughterhouse workers, which decisively increased the pressure on policy-makers. The article contributes to the approach of discursive power resources and strategic framing by unions, and elaborates the relevance of the process of gaining discursive power over time as well as the unforeseeable changes that can dramatically increase a union’s chances of political influence. KEYWORDS: coronavirus; COVID-19; power resources; unions; meat industry
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".