Public performance and the challenge of local collective action strategies: Quebec’s experience with an Integrated Territorial Approach
Bibliographic record
Abstract
Evaluating the performance of local environment activation strategies, set forth in many central policies, is an exercise fraught with challenges. Based on an analysis of 10 Integrated Territorial Approach initiatives, which were rolled out in Quebec’s fight against poverty, this article proposes a framework to better assess their various effects. These strategies are characterised by a partnership process and a collective focus. Performance occurs at micro-, meso- and macro-levels and is observable not only in the production of deliverables, but also on three other process dimensions, which are characteristic of such strategies: fostering the maintenance of local mobilisation drivers; improving the quality of locally productive elements; and learning strategic coherence. This understanding of collective performance takes public managers out of their comfort zone. Beyond having to develop collaborative skills, as is now well-documented in the literature, it leads them to develop an investor mindset and to become logisticians of the collective, not just efficient service providers. Points for practitioners Public managers sometimes feel disadvantaged in local collective action strategies because their performance depends on the contribution of a number of partners whose actions are driven by logics that differ from theirs. By outlining the many possible facets of performance in local collaborative strategies, the statements made in this article could give them greater peace of mind, as well as point out the systemic stumbling blocks that they may face.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".