Stages of food security: A co-produced mixed-methods methodology
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
This article presents the stages of food security methodology, an adaptation of stages of progress developed by Dr. Krishna. Studies of food security are primarily survey based, applying a common set of generalist indicators across a range of agroecological areas and for a diverse array of people; these findings have provided a wealth of information and insight into the trends, challenges and the extent of food security on national, regional and global scales. Ethnographic and qualitative approaches have provided detailed, contextualized findings about the interrelated and complex nature of food security at the micro level. This co-produced, mixed methods approach brings together participatory qualitative approaches and co-produces quantitative data collection tools, which provide generalizable data geared towards supporting the development or refinement of policies and programmes to strengthen food security. Based upon a pilot implementation of the methodology in Ethiopia, advantages and limitations are discussed, as well as reflections on why co-production as a participatory approach was adopted, in contrast to other participatory processes. The findings demonstrate the ways in which co-produced approaches can offer unique insight, complementing and enhancing existing knowledge about complex challenges.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it