A social network analysis of Canadian food insecurity policy actors
Bibliographic record
Abstract
PURPOSE: This paper aims to: (i) visualize the networks of food insecurity policy actors in Canada, (ii) identify potential food insecurity policy entrepreneurs (i.e., individuals with voice, connections, and persistence) within these networks, and (iii) examine the political landscape for action on food insecurity as revealed by social network analysis. METHODS: A survey was administered to 93 Canadian food insecurity policy actors. They were each asked to nominate 3 individuals whom they believed to be policy entrepreneurs. Ego-centred social network maps (sociograms) were generated based on data on nominees and nominators. RESULTS: Seventy-two percent of the actors completed the survey; 117 unique nominations ensued. Eleven actors obtained 3 or more nominations and thus were considered policy entrepreneurs. The majority of actors nominated actors from the same province (71.5%) and with a similar approach to theirs to addressing food insecurity (54.8%). Most nominees worked in research, charitable, and other nongovernmental organizations. CONCLUSIONS: Networks of Canadian food insecurity policy actors exist but are limited in scope and reach, with a paucity of policy entrepreneurs from political, private, or governmental jurisdictions. The networks are divided between food-based solution actors and income-based solution actors, which might impede collaboration among those with differing approaches to addressing food insecurity.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".