Discrepancies in households and other stakeholders viewpoints on the food security experience: a gap to address
Bibliographic record
Abstract
This paper reports results from a case study on household food insecurity needs and the interventions that address them. It aimed at comparing households' perceptions on food insecurity experience and vulnerability to those of other stakeholders: community workers, programme managers and representatives from donor agencies. Semi-structured interviews with 55 households and 59 other stakeholders were conducted. Content analysis was performed, using a framework encompassing food sufficiency, characterization of household food insecurity and vulnerability of households to food insecurity. Overall, the results draw attention to a gap between households and the other stakeholders, where the later do not seem always able to assess the realities of food-insecure households. Other areas of divergences include: characteristics of food insecurity, relative importance of various risk factors related to food insecurity and the effectiveness of the community assistance to enhance the households' ability to face food insecurity. These divergent perceptions may jeopardize the implementation of sustainable solutions to food insecurity. Training of stakeholders for a better assessment of households' experience and needs, and systematic evaluation of interventions, appear urgent and highly relevant for an adequate response to households' needs. Collaboration between all stakeholders should lead to knowledge sharing and advocacy for policies dedicated to poverty reduction.
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".