How Does Context Contribute to and Constrain the Emergence of Responsible Innovation in Food Systems? Results from a Multiple Case Study
Bibliographic record
Abstract
Organizations and practices that contribute to the resolution of major societal challenges are key to achieving a transition towards sustainable and resilient food systems. Previous research identified contextual elements that affect the emergence of organizations and practices with responsibility characteristics, but how this process unfolds remains poorly articulated. Our study thus focuses on how contextual dimensions may contribute to or constrain the emergence of responsibility in food systems. We applied a multiple case study design and conducted 34 semi-structured interviews with 30 organizations in the province of Québec (Canada) and in the state of São Paulo (Brazil). Our across-case analyses clarify how multiple contextual dimensions both contribute to and constrain the emergence of responsibility. More specifically, our findings show that while contextual dimensions shaped by the dominant food system constrain the emergence of responsibility, the same dimensions also contribute to it when they embed responsibility principles. One key contribution of our study is to show that interpersonal relations are an important mediation mechanism that helps to modify contextual elements, so they can contribute to the emergence of responsibility. This study’s findings can inform research and policy aiming to design institutional environments that promote a transition towards more responsible food systems.
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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 | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".