A multidimensional approach to measuring household food security in Taraba State, Nigeria: comparing key indicators
Bibliographic record
Abstract
This study used household data from Taraba State, Nigeria, to explore the advantages of using a multidimensional approach to measure food and nutrition insecurity. Adaptations of three popular food security indicators were combined in a single household questionnaire to test how well the Household Food Insecurity Access Scale (HFIAS), the Dietary Diversity Score (DDS), and the Coping Strategies Index (CSI) complement each other. Sixty-nine per cent of households in the sample were classified as extremely food insecure, which means they are likely to resort to intensive but erosive coping strategies and lower dietary diversity. The three indicators powerfully complemented each other. This multidimensional food security measurement framework provided a more nuanced picture of the depth and breadth of food insecurity for local government areas in Taraba State. This approach can help Nigerian policy authorities overcome the information deficits that impede effective food and nutrition assistance interventions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".