Bibliographic record
Abstract
What Card, Krueger and the research that follows tell us is that labor markets are a lot more complicated than we thought, that market power matters a lot and that there may be much more room for public policy to raise wages in general than Econ 101 would have it. (Paul Krugman, The New York Times, 19 March 2019) ‘I think $15 may be enough to have a life and have the necessities.’ It was as simple as that. It wasn’t an MIT calculation. (Fight for $15 organiser, Kendall Fells, on the determination of the $15 minimum wage goal, in Greenhouse 2019, p 235) The liberal states never fully developed social democratic institutions like some European and all the Nordic countries did. This was not because there was a universal commitment to the institutionalisation of a market-driven liberal ethos. Unions and progressive parties sought to build social democracies. But resistance from the right was tougher, electoral arrangements favoured the political right, and industry would not tolerate state coordination of markets. Across employment and welfare policy, the US made the least progress, failing to develop the national institutions after World War Two. This left the country with, as Weir (1992, p 4) puts it, a ‘truncated repertoire of policies to deal with employment issues’. The same can be said about social welfare. Of course, the antipodean states, the UK, and Canada all went further, building employment and welfare state institutions that reflected the power resources of labour and the political left. Still, the ‘truncated repertoire’ remains an enduring problem across the liberal world. Broader institutional delay and half-measures illustrate these problems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".