Contemporary employers' organizations: Adaptation and resilience
Bibliographic record
Abstract
This book argues that employers’ organizations are resilient organizations that adapt to changing circumstances by developing new practices. Adaptation has been prompted by changing economic and social contexts, including state interventions and union activities. Contexts vary over time, across countries and world regions. The purpose of the book is to explore these variations and their impacts on employer organization. The book covers the following themes across four book sections: theoretical perspectives on employer collective action; employers’ organizations in different types of capitalism; different types of employers’ organizations; and international and comparative employer interest representation. Theoretical explorations examining employer power, political preferences, meta-organizing, and ideological foundations are complemented by studies of employers’ organization in China, Denmark, Australia, Germany, Turkey, Canada, and the UK. Different types such as regional and international employers’ organizations are also examined. The book is one of the few edited volumes to examine employer collective action within work and employment, and is the first since 1984 to consider western and non-western contexts. The book will be of interest to employment relations and sociology of work researchers, scholars, advanced students, and practitioners as it brings new perspectives to an understudied actor in employment relations: employers’ organizations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".