The Politics of Children’s Services Reform: Re-Examining Two Decades of Policy Change, Carl Purcell
Bibliographic record
Abstract
There is no denying that politics, politicians and political agendas have had a profound impact on personal services for several decades (Jones, 2020). Purcell takes us through an insightful journey of the particular role that specific governments can have, but, perhaps most importantly, how the agendas of politicians can affect the course, funding and priorities of children’s services. Purcell presents a thorough and coherent story about how agendas are managed, implemented and funded, while helping the reader to understand the power of the central government to set targets that local administrations must grapple with. Purcell has laid the chapters out into two vibrant sections. The first looks at the reforms under the Labour Governments of 1997–2010 while the second looks at the coalition and Conservative governments of 2010–2019. The strength of these period reviews is the draw upon primary sources and interviews of key players. The reader thus...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".