Smart Practices to Smart Results
Bibliographic record
Abstract
This paper outlines the innovative research projects, processes and organizations created by public sector managers in Canada to identify citizens’ service needs and expectations, and to forge “smart practices ” (Bardach 1998) in citizen-centred service delivery. These smart practices, which have been recognized by national (IPAC) and international (CAPAM) awards, have resulted in a significant improvement in citizen/client satisfaction with Canadian public sector service quality since 1997. In the eyes of citizens, Canadian public sector services now match citizens ’ satisfaction with private sector service quality. In 1997, following a decade of service improvement activity by the Government of Canada, it appeared to public service leaders that citizens had not noticed a significant improvement in service delivery. Therefore, the Honourable Jocelyne Bourgon, head of the Canadian Public Service, asked the Canadian Centre for Management Development (www.ccmd-ccg.gc.ca) to apply its action research methodology to find a way to significantly improve citizens ’ satisfaction with public sector services. According to Stephen Corey, action research is “the
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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 | 0.040 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.085 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".