Scaffolding collective agency curriculum within food-systems education programs
Bibliographic record
Abstract
Collective agency (CA) can be defined as the shared understanding, will, and ability of a heterogenous group to take action and work together toward a common goal. We are motivated by the premise that CA is central to meeting the challenges inherent to 21st century food systems. These challenges include maintaining sustainable agricultural production and meeting nutritional needs of a growing population while protecting the climate, wildlife, soil, air and water quality, and enhancing equity, inclusion and justice for those who work in or engage with these systems. Given the importance of CA in food systems, university programs focused on food systems must address it. To date, despite many calls for higher education to build skills in CA, implementation has been minimal. Single courses addressing CA exist in some program-level curricula, but we know of no previous efforts in food-systems degree programs to systematically cultivate CA across their curriculum through scaffolding, i.e., interconnection and integration of learning activities across courses, so as to enhance their complementarity and impact. We (a consortium of university faculty building food systems curricula, located at University of British Columbia, Montana State University, and University of Minnesota) developed our approach to teaching CA through an action-research process, conducted during 2019–2022. In this paper, we report on our process and outline an emergent conceptual model of a curriculum for CA that can be embedded within broader, program-level food systems curricula. We describe its elements and share our experiences in implementing these elements. We conclude by describing current efforts to further develop CA curricula in the context of food-systems degree programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".