Coalition Building and Maintenance: The case of Food Secure Canada (2001-2012)
Bibliographic record
Abstract
This study deals with the question of advocacy coalition formation and maintenance, in the specific case of Food Secure Canada (FSC), a pan-Canadian alliance of non-profit organizations and individuals working together to advance food security and food sovereignty in Canada. Using theoretical frameworks from literature on the Advocacy Coalition Framework and Resource Mobilization Theory, this dissertation provides a case study of FSC. Examining food civil society organizations in Canada from the 1970’s onward, this study provides insights on the social, economic and political context that surrounded the formation of FSC as an advocacy coalition. Through review of existing reports and documents produced by FSC and 21 semi-structured interviews this project provides insights into the role of coalition building and maintenance. The study provides insights on how advocacy coalitions form, maintain unity and deal with internal differences and how they utilize resources in overcoming organizational challenges. This study also explores how FSC built consensus around its three goals -zero hunger, a sustainable food system, and healthy and safe food - between 2001-2006 and how it managed to stir its Policy Framework of food security to food sovereignty between 2006-2012. This case study, will contribute to the literatures on food policy and advocacy coalitions with a focus on the role of coalition building and maintenance in the policy making process.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.056 | 0.015 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".