Bibliographic record
Abstract
When the Labour Party was swept back into power on 1 May 1997, with an unprecedented majority and the realistic prospect of securing two full terms in government, the reform of the welfare state was high on its to-do list. What this actually meant was not always clear. Reportedly, when Tony Blair called Alan Milburn three days after the election to invite him to be the new minister of state for health, he told him ‘We haven’t got a health policy … Your job is to get us one’ (Timmins, 2017: 589). But the broad agenda for welfare, and its centrality to the New Labour project, seemed clear enough. Three of the five pre-election pledges made by the party concerned welfare: reducing class sizes, shortening NHS waiting lists and getting 250,000 young people off benefits and into work. There were two key components to this agenda: the first was to encourage the move ‘from welfare to work’, captured in the mantra ‘work for those who can, security for those who cannot’; the second was to direct greater resources into public services, and to achieve a step-change in the quality of provision.1 As we already have a number of excellent accounts of this reform programme, I confine myself here to a brief reminder of Labour’s policy agenda (among others, Ludlam and Smith, 2004; Powell, 2008; Timmins, 2017). I devote rather more attention to the outcomes of these reforms, as this has been less comprehensively covered and is the source of considerable misunderstanding – and misrepresentation.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 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".