Gender Bias in Agricultural Child Labor: Evidence from Survey Design Experiments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Agricultural labor accounts for the largest share of child labor worldwide. Yet, measurement of farm labor statistics is challenging due to its inherent seasonality, variable and irregular work schedules, and the varying saliencies of individuals’ work activities. The problem is further complicated by the presence of widespread gender stratification of work and social lives. This study reports the findings of three randomized survey design interventions over the agricultural coffee calendar in rural Ethiopia to address whether response by proxy rather than by self-report has effects on the measurement of child labor statistics within and across seasons. While the estimates do not report differences for boys across all seasons, the analysis shows sizable self/proxy discrepancies in child labor statistics for girls. Overall, the results highlight concerns on the use of survey proxy respondents in agricultural labor, particularly for girls. The main findings have important implications for policymakers about data collection in rural areas in developing countries.
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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 | 0.002 | 0.000 |
| 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) | 0.002 | 0.001 |
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 it