Safer and Stronger? The Decline of Managerial Competence and Liberal Welfarism in Justice Policy
Bibliographic record
Abstract
Since the SNP came to power in 2007, they have sought to pursue two objectives with respect to matters of justice: to demonstrate managerial competence; and to ‘re-tartanise’ Scottish justice policy. While the headline figures present a generally positive figure of the SNP's nine years in government, belying these figures is an increasing tendency towards illiberal and authoritarian justice policies, as well as mismanagement on the part of ministers. This article considers the SNP's approach to and management of justice policy, and whether or not they have been successful in the pursuit of their twin objectives. It considers the degradation of ministers’ once-strong relationship with the legal professions, the management of the Crown Office and Procurator Fiscal Service, the establishment of Police Scotland, and the Scottish Ministers’ increasing deference to the police on ‘operational matters’. It further considers the continuation of the ‘ned-bashing’ agenda of the Scottish Government and concludes that, while ministers might rhetorically seek to appear liberal and welfarist, in contrast to England and Wales, the reality has been the pursuit of punitive policies that are arguably even less liberal, and less welfarist, than that of their predecessors, or their counterparts in England and Wales.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".