The Hunger of Old Women in Rural <scp>T</scp>anzania: Can Subjective Data Improve Poverty Measurement?
Bibliographic record
Abstract
On average, women in T anzania are slightly less likely than men to say that they are “always/often without enough food to eat”—but this masks a much higher rate of self‐reported food deprivation among elderly rural women. Official T anzanian poverty statistics are, however, based on a methodology which presumes equal sharing per equivalent adult within the household. This paper combines subjective and objective micro‐data from T anzania's 2007 Household Budget Survey and 2007 Views of the People Survey. By imputing individual consumption based on the relative probability of self‐reported food deprivation, it provides an example of the possible importance of one type of intra‐household inequality—i.e., the hunger of old women—for poverty measurement. Implications include the complexity of gendered intra‐household inequality and the importance of “technical” poverty measurement choices for public policy priorities, such as old age pensions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".