Bibliographic record
Abstract
academic programs in history of industrial relations 18-26, 143-8 problems of interdisciplinary industrial relations 170-71 science-building 34-5, 42-3, 147-8, 156, 165 social capital and labor movement decline in United States 94, 95, 104-5, 107 see also business schools; business schools and marginalization of industrial relations in Canada; degrees; extension programs; Immigrant Worker Resource Center (IWRC); MBAs; PhDs; UCLA Labor Center; universities Academy of Management (AOM) 19, 129, 131, 146, 147 accreditation, business schools in Canada 125, 126 actors 70, 71, 230-34 see also employers; government; organizations; unions administrative law in the United States 78, 79-80 adversarial employment relationships 71, 75, 79 AFL-CIO 20, 149, 151, 205, 206, 212, 216 Age Discrimination and Employment Act (1967) 118 agency 180-181 see also control; power relations alternative work practices (AWPs) 76 American Arbitration Association (AAA) 95, 98-100, 106 American Economic Association (AEA) 9, 144 American Manufacturing Association (AMA) 95, 103 American Racing Equipment Company 213-14 Americans for Democratic Action (ADA) 95, 102 Andrews, John B. 16 anti-discrimination policies and laws 118, 119, 229-30 anti-sweatshop movement 199-200, 202-3, 204-6, 207, 208 apparel industry, women's see labor relations in the women's apparel industry arbitration 98-100, 117-19, 120 Asia 127, 128, 184, 187, 198, 199, 203, 204, 218 see also Asian market economies (AMEs); Cambodia; China; India; Japan; South Korea; Taiwan Asian market economies (AMEs) 182-3, 189 Assembly Bill (540) 221 Association for Labor Relations Agencies (ALRA) 95, 101 Association for Union Democracy (AUD) 95, 102-3 association journals 149-50, 156 associations 94, 95, 98-101, 106, 107 see also association journals; employer associations; organizations; individual associations Australia 175, 179, 182, 186, 187, 188, 189 automotive industry see globalization and employment relations in the automotive 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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.411 | 0.181 |
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".