Considering gender differences in measuring household food insecurity in northern Ghana
Bibliographic record
Abstract
This study compares estimates of household food insecurity between men and women living within the same household (n = 866) to assess whether there is a gender bias in reporting. The main research question is, do household food insecurity scores and prevalence categories differ between male and female spouses within households in the sample? Findings indicate that men's household food insecurity estimates were lower on average at 3.49, than women's estimates at 5.06. There is also a statistically significant decrease in men's estimates when compared to women's. Overall, these findings question the reliability of household-level food insecurity measures that rely on heads of households' estimations by pointing to discrepancies found in this reporting between husbands and wives within the same household. Since this study sampled married women and men within the same household, gender differences found are also more directly attributable to gender than in most other studies that compare male and female-headed households' food insecurity reporting. Though further assessments across other cases are needed, more reliable measures of household food insecurity could include averaging estimates of multiple individuals within households. Qualitative research into the gendered dynamics could also improve sampling and the interpretation of findings from surveys on household-level measures.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".