How poor law rights were lost but Victorian values survived: a reconsideration of some of the hidden values of welfare provision
Bibliographic record
Abstract
At first glance, an historical approach to modern welfare law provision in England and Wales that links welfare with the poor law may seem an intellectual indulgence. This chapter will argue that an understanding of welfare’s legal history provides an explanation for many of the legal presumptions inherent in contemporary law and for the manner in which that law has developed as public law. It will suggest that the old poor law personal right to relief might produce a hardier welfare model than that currently available based upon the Beveridge Report. This chapter will take a broad contextual approach to the history of welfare, drawing many strands together, including its ecclesiastical and customary welfare elements, to illustrate certain themes that the modern welfare lawyer will recognise. The writer’s indulgence in history may seem greater as few law graduates have encountered the history of law during their studies since, unfortunately, legal history is a discipline in search of a home in mainstream academic legal studies. It rarely forms part of the undergraduate law curriculum in the universities of the UK, and is found as part of taught masters degrees, for the most part, only in our most ancient and/or prestigious institutions. This is not true of the United States and Canada where undergraduate courses in legal history abound.2
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.039 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".