Towards an equity competency model for sustainable food systems education programs
Bibliographic record
Abstract
Addressing social inequities has been recognized as foundational to transforming food systems. Activists and scholars have critiqued food movements as lacking an orientation towards addressing issues of social justice. To address issues of inequity, sustainable food systems education (SFSE) programs will have to increase students’ equity-related capabilities. Our first objective in this paper is to determine the extent to which SFSE programs in the USA and Canada address equity. We identified 108 programs and reviewed their public facing documents for an explicit focus on equity. We found that roughly 80% of universities with SFSE programs do not provide evidence that they explicitly include equity in their curricula. Our second objective is to propose an equity competency model based on literature from multiple fields and perspectives. This entails dimensions related to knowledge of self; knowledge of others and one’s interactions with them; knowledge of systems of oppression and inequities; and the drive to embrace and create strategies and tactics for dismantling racism and other forms of inequity. Integrating our equity competency model into SFSE curricula can support the development of future professionals capable of dismantling inequity in the food system. We understand that to integrate an equity competency in our curricula will require commitment to build will and skill not only of our students, but our faculty, and entire university communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".