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Record W4312496988 · doi:10.15173/glj.v13i3.4768

The Influence of the Discursive Power of Unions in the Swift Re-regulation of Slaughterhouse Labour during the COVID-19 Crisis in Germany

2022· article· en· W4312496988 on OpenAlexvenueno aff
Martin Seeliger, Marcel Sebastian

Bibliographic record

VenueGlobal Labour Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsFraming (construction)GermanCoronavirus disease 2019 (COVID-19)Power (physics)CriticismPolitical sciencePoliticsPolitical economySociologyLawInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

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

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.288
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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