Mikrocensus 1996, 2. quarter: Birth-Biography, Educational Career
Bibliographic record
Abstract
This Mikrozensus special survey consists of 2 parts which both have population statistical background. On question block is on birth-biography (which includes questions on the frequency of marriages) and the other is on the educational career. The block on the birth-biography is not new since it is identical to the one from the surveys in June 1991 (Mikrozensus MZ9102) and June 1986 (Mikrozensus MZ8602) and similar to those from 1981 (Mikrozensus MZ8102) and 1976 (Mikrozensus MZ7602). The family is currently in a state of flux. The birthrate today is much lower than in past times. The number of new marriages is declining while the number of young people who stay single increases. Many people live in extramarital life partnerships. On the other hand, divorce rates have increased so much that nowadays every third married couple gets a divorce. To add numbers to these undeniable trends it is necessary to have reliable information from the Mikrozensus standard surveys complemented by Mikrozensus special surveys. The topic educational career has never been covered so thoroughly in any Mikrozensus, although some questions had been part of past special programs, e.g. in the special program advanced education from December 1989 (Mikrozensus MZ8904). There is no available up-to-date data which would give sufficient information for various problems (e.g. Europe-wide programs with educational-political goals or labour force research in Austria) since results from the institutionalised educational statistics (school- and high-school statistics) as well as the population census offer only limited information.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.042 | 0.017 |
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".