Bibliographic record
Abstract
This chapter presents a limited comparative analysis in order to explore the potential transferability of the multidimensional framework. It considers five countries, namely, China, France, Germany, Japan and US. Whereas a single Chinese union projects a ‘true general union identity’ and operates in parallel with the Communist Party, the multiplicity of competing French unions project identities that can only be understood with the addition of political and religious sources. Although most German unions project industrial identities, for some a religious or professional component is required. The multi-layered structure of Japanese union organisation includes unions that project ‘organisational union identities’ in the corporate sector and ‘industrial union identities’ in the public sector and public services. The majority of US unions project occupational and/or industrial identities, although many also have binational identities, with membership territories incorporating the US and Canada. In contrast the Teamsters and IWW project ‘general union identities’ and a more militant version of ‘protest union identity’. Whilst the chapter concludes that the multidimensional framework is broadly applicable to unions in other countries it identifies that additional sources of identity are needed for comparative analysis.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".