Advances in Industrial & Labor Relations
Bibliographic record
Abstract
Volume 14 of Advances in Industrial and Labor Relations (AILR) contains 10 papers dealing, respectively, with HR versus finance in the control of corporate health care decisions; a theory of workplace conflict grounded in U.S. municipal collective bargaining; creative compliance in, or union defiance of, labor regulation in Australia; the extent to which union organizing means determine bargaining ends; the failure of labor-manangement cooperation at two Maine (U.S.) paper mills; the interplay between union and nonunion representation arrangements at Eurotunnel; challenges to and prospects for the industrial relations field in France; an empirical and comparative analysis of the industrial relations field in Germany; the development of the industrial relations field in Canada; and the implications of a decentralized labor market for industrial relations as a field in Australia. Taken together, these papers feature a rich mix of theory and empiricism, quantitative and qualitative analyses, and international perspectives on both industrial relations and human resources. Four of the papers were winners of the 2004 and 2005 AILR/Labor and Employment Relations Association Competitive Papers Competitions, and all papers were subject to double blind anonymous refereeing. The papers in Volume 14 of AILR will be of interest to industrial relations and human resource scholars and practitioners worldwide
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.020 |
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".