Representative time use data and new harmonised calibration of the American Heritage Time Use Data (AHTUD) 1965-1999
Bibliographic record
Abstract
Representative and reliable individual time use data, in connection with a proper set of socio-economic background variables, are essential elements for the empirical foundation and evaluation of existing and new theories in general and in particular for time use analyses.Within the international project Assessing Time Use Survey Datasets several potentially useful individual US time use heritage datasets have been identified for use in developing an historical series of non-market accounts.In order to evaluate the series of American Heritage Time Use Data (AHTUD) (1965, 1975, 1985, this paper analyses the representativeness of this data when using given weights and provides a new harmonised calibration of the AHTUD for sound time use analyses.Our calibration procedure with its ADJUST program package is theoretically founded on information theory, consistent with a simultaneous weighting including hierarchical data, ensures desired positive weights, and is well-suited and available for any time use data calibration of interest.We present the calibration approach and provide new harmonised weights for all AHTUD surveys based on a substantially driven calibration framework.To illustrate the various application possibilities of a calibration, we finally disentangle demographic vs. time use behavioural changes and developments by re-calibrating all five AHTUD surveys using 1965 population totals as a benchmark.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".