Bibliographic record
Abstract
Undertaking this study was the fulfillment of a long-time desire to follow-up on work initiated in Western Kenya in the mid-1990s using orange-fleshed sweetpotato as an entry point for addressing vitamin A deficiency in rural sub-Saharan Africa. First and foremost, I would like to thank Venkatesh Mannar, the president of the Micronutrient Initiative (MI) of Canada for his willingness to fund an integrated food-based action research study at a time when food-based initiatives had fallen out of favor with many donors. MI’s strong support encouraged others to participate. Hopefully, these findings will provide valuable lessons regarding the integration of nutritional concerns into agricultural projects in the search for effective means for addressing the underlying causes of poverty and malnutrition. Three years is a very short-time frame to design, implement, and write-up results of an intervention that inherently depends on the agricultural cycle and unpredictable weather. It was only possible due to strong partnerships, supportive colleagues and friends, a dedicated staff, and understanding donors. Lourdes Fidalgo, former head of the Nutrition Division, was instrumental in helping to get the project initiated and provided
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.008 |
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".