Bibliographic record
Abstract
The first four chapters in this book have dealt largely with some of the problematizations out of which the modern conception of unemployment and unemployment policy arose. The previous chapter introduced a range of more contemporary concerns, albeit largely from the perspective of the different spatializations of the unemployment question presupposed by various governmental programmes and strategies. In this final, substantive chapter I want to complement my historical focus with a case study of a very recent initiative within unemployment policy – the Labour Government's New Deal for the unemployed. Labour has presented this as ‘[t]he largest assault on structural unemployment ever undertaken in this country’. It would be misleading, however, to suggest that this chapter was somehow bringing our discussion of unemployment ‘up to date’. For this study does not pretend to be a comprehensive history of unemployment policy. Rather, it should be read as a series of strategic encounters with historical materials, engagements which are intended to sharpen our comprehension of the present. One of the central tasks of this book has been to enhance our understanding of what it means to govern unemployment from a ‘social’ perspective. The contribution of this chapter is to assess whether the approach to the unemployed which is currently fashionable with governments, an approach variously described as ‘workfare’ or ‘welfare-to-work’, and which the New Deal seems to exemplify, can still be considered a form of social governance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".