Bibliographic record
Abstract
wage 34, 55 trade union 17, 114, 139 see also Fight for $15 movement Aid to Families with Dependent Children (AFDC) 68 Alberta 55 Amazon 16 American Federation of Labor 83 Anker, Richard 7, 8 Anti-Sweating League 82 Ardern, Prime Minister Jacinda 19, 53-4, 136, 171, 175-6 Argentina 156 artificial intelligence 160 associational action 139-41 Atkinson, A.B. 108 austerity policies 147-9 Australia 20 austerity wage policies 93 award wages system 5, 21, 22, 39, 40, 49, 52, 83, 88 benefits from 143 casual employment 76 Coalition government 139 employment performance 117, 119 even distribution of full-time earnings 138 Fair Work Act (2009) 52-3, 127 Fair Work Commission 39, 52, 53, 143 fiscal deterioration and spending 148 gig employment 78 housing costs 63 JobSeeker 70 Kaitz score 20, 101, 133, 139 Labor Party 139 low-wage workers 44, 45, 46, 138 middle classes in 31 minimum wage-setting institutions 49-54 minimum wage workers 39-40 minimum wages 81, 139, 144 political developments 139 poverty 73 pro-worker reforms 127 underemployment problems 138 underpayment of workers 163 union density 125 union rights 125 welfare dependency and 72 welfare reforms 70, 71-2 WorkChoices 52 worker voice and protections 124 Austria, middle classes in 31 automation 2, 158-62 automatable tasks 159-60 differing perceptions of 162 empirical research on 159 living wage movement and 161-2 media attention 161 occupations susceptible to 159, 160 threats of 161 award wages system in Australia 5, 21, 22, 39, 40, 49, 52, 83, 88 in New Zealand 49, 53, 83, 93 B ballots 130 basic income 2-3, 166-9 attraction of wider audience 11 compromises in implementation phase 168-9 critics of 167 jobless future, forecasts of 167 Marxist perspective 167-8 political left perspective 2-3
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.865 | 0.801 |
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".