A Participatory Food Costing Model: In Nova Scotia
Bibliographic record
Abstract
In recognition of the growing challenge that food insecurity has on population health, a multisectoral partnership in Nova Scotia has been working since 2001 to address province-wide accessibility to a nutritious diet. The participatory food costing (PFC) model has been at the forefront of provincial and national efforts to address food insecurity; a local foods component was incorporated in 2004. This model has engaged community partners, including those affected by food insecurity, in all stages of the research, thereby building capacity at multiple levels to influence policy change and food systems redesign. By putting principles of participatory action research into practice, dietitians have contributed their technical, research, and facilitation expertise to support capacity building among the partners. The PFC model has provided people experiencing food insecurity with a mechanism for sharing their voices. By valuing different ways of knowing, the model has facilitated much-needed dialogue on the broad and interrelated determinants of food security and mobilized knowledge that reflects these perspectives. The development of the model is described, as are lessons learned from a decade of highly productive research and knowledge mobilization that have increased stakeholders' understanding of and involvement in addressing the many facets of food security in Nova Scotia.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| 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".