Does It Matter Who You Ask For Time Use Data?
Bibliographic record
Abstract
Abstract Time-use statistics are recall intensive and sensitive to measurement error. This study uses a nationally representative time-use survey from India to investigate how self and proxy reporting impacts the reported time spent on various activities by men and women. Proxy informants tend to report higher time use for both men and women on employment activities (14 to 26 percent) and lower time use on production for self-consumption, unpaid domestic work, and care work (5 to 33 percent) as compared to self-reports. On average, women proxies differ more from self-reports when reporting about both men and women in their households as compared to men proxies. Investigating the mechanisms we find that the self–proxy differences are systematic and not attributable solely to random measurement error. Information asymmetry between the self and proxy respondents plays a key role—spouses and self–proxy respondents with similar characteristics have smaller reporting differences than non-spouses and other respondents. Gendered perception of what activities are classified as work influences the differences in reporting, which highlights asymmetric measurement error.
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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 | 0.012 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".