Bibliographic record
Abstract
This paper reports a multi-year design-based implementation research (DBIR) that examines practical issues, challenges, and innovations faced by the Montreal food polity in transforming food systems for alleviating insecurity in vulnerable populations. Community organizations in three geographically distinct neighbourhoods were engaged in three distinct city-level collaborative engagement initiatives (coalition of neighbourhood roundtables; place-based philanthropy initiative-CIP; food system policy council-C-SAM). The later city-level initiatives stemmed from different historical and institutional contexts and afforded different types and amounts of capabilities in support of community organizations. Our results underscore the rich diversity not only in how local communities organize themselves over time but also how they welcome or not scaling-up or capacity building initiatives like CIP and C-SAM. As part of the same complex and dynamic adaptive system observed at any stage of its evolution, individual organizations and collaborative platforms observed in this research were all having their respective historical trajectories and future aspirations in terms of composition, capabilities, goals, achievement, and challenges. Contributions to food systems research are three-fold: Isomorphism, Discursive Frame, and Decoupling between Norms and Action. Our research demonstrates that neighborhoods, like nation-states, exhibit different pathways to adoption, adaptation, and decoupling action from norms when cities become part of an international regime. The outcome of cities signing on to new international agreements are similarly symbolic in nature. Yet organizations and neighborhoods respond to these by adopting the discursive agendas of these new norms while, at the same time, exhibiting different pathways in policy and planning depending on their neighborhood histories, structure, and capacity. We close with a discussion of different path dependencies that vary by location.
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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 | 0.038 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".