Bibliographic record
Abstract
The Social Fund has long been criticised by researchers and practitioners for failing to meet the needs of people in poverty. The first piece of research highlighted here from the National Association of Citizens Advice Bureaux (NACAB) once again details the major flaws with the Social Fund. How long can the government hold out against reforming this Cinderella aspect of the benefit system? Tax Credits have only just been introduced (as of April 2003) and so we do not know how they might work in practice. Having said this, the newspapers were full of stories of long delays as the Inland Revenue grappled with this huge administrative task. Work funded by the Joseph Rowntree Foundation argues that the UK Tax Credits have been designed to avoid the controversial overpayments which are a feature of the Australian system and the lack of responsiveness as in Canada. We will not know whether or not the UK system achieves this for some time. The final piece of research here is a very positive assessment of the New Deal for Lone Parents. Readers might be suspicious of the rosy picture painted by the research given that it was funded by the Department for Work and Pensions (DWP) itself. But having worked for DWP myself in the past, and knowing that the authors of the report are respected colleagues of mine at the University of Bath, I think we can be confident in the independence of the authors’ conclusions!
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.615 | 0.437 |
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".