Modularity in Intersectoral Research/Action Collaborations for Food Systems Transformation: Lessons from the FLEdGE Community-Engaged Network
Bibliographic record
Abstract
How can academics and community practitioners better collaborate to overcome the existing barriers? What role can intersectoral research collaboratives play in supporting, enhancing, and sustaining the impact of community-engaged research? In response to these broad questions, this paper shares insights from the Food: Locally Embedded, Globally Engaged (FLEdGE) community-engaged research network, a collaborative, interdisciplinary group of scholars and practitioners that crossed sectors, scales, and geographies. The FLEdGE research program ran from 2015 to 2021, and built on over a decade of academic and community partnerships to assess the current and potential role of food initiatives as pillars for sustainable transformation. Our mixed-methods study draws on data from a social network analysis survey, summary reports, semi-structured interviews, and reflections from the authors who were all active members of the network. Our findings reveal that beyond making theoretical and practical contributions to food systems scholarship and initiatives in the participating regions, FLEdGE played an important role in building food movements across Canada and beyond. We describe this as a modular approach, an organizational structure in which multiple units (or modules) operate independently while also sharing enough commonalities that allow them to be interrelated, modified, and reconfigured in diverse and dynamic ways. We argue that intersectoral research networks adopting a modular approach require interdisciplinarity and collaborative methodologies, but also flexibility and critical reflexivity. In addition, we underscore that setting objectives, both overarching and tactical, requires a negotiated approach, particularly when budgetary administration resides within an institutional partner.
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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 | 0.080 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.017 | 0.070 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.004 | 0.005 |
| 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".