Policy analysis Drug testing and mandatory treatment for welfare recipients
Bibliographic record
Abstract
One province in Canada, Ontario, is considering the use of drug tests for welfare recipients. Those with positive tests could be required to receive treatment and abstain from drug use or risk losing their benefits. Several experts from the Centre for Addiction and Mental Health (CAMH) reviewed the scientific strengths and weaknesses of this proposal. Strengths included possible increases in employment and reduced drug use among welfare recipients; however, the group concluded that drug testing of welfare recipients or removal of welfare benefits for people who refuse treatment or relapse is not advisable for several reasons. Drug testing cannot be used to determine substance abuse or dependence, could undermine the client case manager relationship and could be legally challenged as a violation of human rights. Other drawbacks of conditional welfare include possible negative societal consequences (i.e. increased crime and health problems) and disruptions to the treatment population. The whole process is expensive and will likely result in a very marginal increase in employment because drug dependence is not a major barrier to employment. © 2001 Elsevier Science B.V. All rights reserved.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.014 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 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".