Beyond new public management: Empowering community‐based organisations
Bibliographic record
Abstract
AIMS: To describe how new public management practices, a global public service management trend, and a provincial community of practice, a group of people who learn from each other by interacting on an ongoing basis, affected a group of 240 community-based organisations. METHODS: We conducted a holistic single case study of 240 grassroots, community-based organisations called Family Resource Centres in the province of Québec, Canada. Data was collected from 36 research interviews, 6 years of participant observation, institutional documents and a research journal, and analysed qualitatively. RESULTS: New public management practices foster social injustice and endanger the integrity of the community-based organisations, whereas the provincial community of practice empowered them to fight back deleterious new public management practices and reclaim their identity. CONCLUSION: A provincial community of practice allowed 240 independent community-based organisations in Québec, Canada to become empowered on a macro level while remaining faithful to their small scale community orientation. We hope this model can serve as an example of alternatives to current (new public) management practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.034 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".