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.
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 | 0.148 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".