Clashes in culture: the professionalisation and criminalisation of the drugs workforce
Bibliographic record
Abstract
In the last decade, the number of people in drugs treatment in England has more than doubled to a total of 207,580 in 2008/9. The increasing access to drugs treatment has been accompanied by an expansion and development of the drugs workforce. This development has taken the form of a ‘professionalizing strategy’ and includes the introduction of national occupational standards to establish levels of competence required of those working in the drug treatment field and enhancement of career pathways. This paper charts the growth of the drugs workforce over time, examines the changes in terms of their training and education, and considers the impact of contemporary policy development on their practice. In particular, it will explore the process of ‘criminalizing’ drugs work and the conflicts and contradictions this has created for those working in the field. The paper will also consider the recent debates relating to the organising ideologies for drug treatment and how the current emphases on recovery, reintegration and personalisation might impact on the training and the practice of the drugs workforce.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.057 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".