Ethics and Leadership in Times of Austerity: Ontario's Courts and "Justice on Target"
Bibliographic record
Abstract
Inefficiencies in the public service are best identified by public servants actually delivering the services. If these employees are convinced that they will be treated with respect— which includes the assurance that they will continue to be employed although possibly in a different capacity—and if these employees have the integrity to care about the public good in contrast to thinking of their employment as merely a job to pay the bills, they are likely to generate ideas that could result in more effective public services at less cost. In addition to input from the field, a successful cost saving strategy requires strong, high profile leadership from top departmental personnel such as the Minister and Deputy Minister. And finally, a successful strategy requires that all key stakeholders are properly consulted, and buy into to the recommended strategy (Gabor and Greene, 2002).This paper tests this argument by analyzing the successes and failures of the Ontario Ministry of the Attorney General’s “Justice on Target” initiative. Announced by the Attorney General in 2008, the goal of this innovation was to reduce by an average of 30 per cent the number of court appearances between the initiation of a case and its disposition, and the average time between initiation and disposition. This goal was to have
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.025 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".