Strengthening the food systems governance evidence base: Supporting commensurability of research through a systematic review of methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Governance of food systems is a poorly understood determinant of food security. \nMuch scholarship on food systems governance is non-empirical, while existing \nresearch is often case study-based and theoretically and methodologically \nincommensurable. This frustrates aggregation of evidence and generalisation. We \nundertook a systematic review of methods used in food systems governance research \nwith a view to identifying a core set of indicators for future research. We gathered \nliterature through a structured consultation and sampling from recent reviews. \nIndicators were identified and classified according to the levels and sectors they \ninvestigate. We found a concentration of indicators in food production at local to \nnational levels and a sparseness in distribution and consumption. Unsurprisingly, \nmany indicators of institutional structure were found, while agency-related indicators \nare moderately represented. We call for piloting and validation of these indicators and \nfor methodological development to fill gaps identified. These efforts are expected to \nsupport a more consolidated future evidence base and eventual meta-analysis.
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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 | 0.020 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it