Index
Bibliographic record
Abstract
AsiaEast Asian developmentalism 27-8, 31 and international trade unionism 280 South Asian migration to UK 145-6 see also individual countries ASP (Australia), see trade unionism, spatiality of, and Pilbara, Action in Support of Partners ATC, see Europe, clothing workers and WTO Agreement on Textiles and Clothing Auger, P. 127 Australia Australian Council of Trade Unions (ACTU) 349, 356, 358, 360, 361 Australian Mines and Metals Association (AMMA) 354 Australian Workplace Agreements (AWAs) 361, 362 Construction, Forestry, Mining and Energy Union (CFMEU) 412 dock workers' dispute 286 Industrial Relations Awards 358, 361 LabourStart and Sydney Hilton workers' campaign 433 Pilbara, Western Australia, see trade unionism, spatiality of, and Pilbara planned company towns and worker governance 194 private equity companies and corporate acquisitions 402 Private Finance Initiatives (PFI) 68 Rio Tinto Global Union Network (RGUN) 412, 413, 414 Rio Tinto, union struggles with 412-17 transnational unions 258 union membership decline 349, 350 union membership and geography 350-51 unionism in Pilbara, Western Australia, see under trade unionism, spatiality of Workplace Agreements (WPAs) 357-9, 360, 361-3 Workplace Relations Act 357-8, 360 Aviva Global Services, and Indian call centres 436, 440 AWAs, see Australia,
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.733 | 0.507 |
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".