Weighting Procedure of the Austrian Microcensus using Administrative Data
Why this work is in the frame
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Bibliographic record
Abstract
The Austrian microcensus is the biggest sample survey of the Austrian population, itis a regionally stratied cluster sample with a rotational pattern. The sampling fractionsdier signicantly between the regions, therefore the sample size of the regions is quitehomogeneous. The primary sampling unit is the household, within each household allpersons are surveyed. The design weights are the input for the calibration on populationcounts and household forecasts. It is performed by iterative proportional tting. Untilthe third quarter of 2014 only demographic, regional and household information wereused in the weighting procedure. From the fourth quarter 2014 onwards the weightingprocess was improved by adding an additional dimension to the calibration, namely alabour status generated from administrative data and available for the whole population.Apart from that some further minor changes were introduced. This paper describes themethodological and practical issues of the microcensus weighting process and the varianceestimation applied from 2015 onwards. The new procedure was used for the rst timefor the forth quarter of 2014, published at the end of March 2015. At the same time, allprevious microcensus surveys back to 2004 were reweighted according to the new approach.
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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 | 0.001 | 0.006 |
| 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 it