QUEST 2003: proceedings of the 4th Conference on Questionnaire Evaluation Standards, 21-23 October 2003
Bibliographic record
Abstract
"This volume, the ninth in the ZUMA-Nachrichten Spezial series on methodological issues in empirical social science research takes up issues of question and questionnaire evaluation. The papers in this volume discuss practical as well as theoretical aspects of questionnaire evaluation. All contributions are based on presentations made at the fourth QUEST (Questionnaire Evaluation Standards) conference which took place from October 21-23, 2003 at ZUMA in Mannheim. There were 26 attendees from 9 countries representing 14 organizations: Bureau of Labor Statistics, USA, Center for Survey Research, University of Massachusetts, USA, Institut für Demoskopie Allensbach, Germany, National Center for Health Statistics, USA, National Center for Social Rerearch, U.K., Office of National Statistics, U.K., Statistics Canada, Statistics Finland, Statistics Netherlands, Statistics New Zealand, Statistics Norway, Statistics Sweden, U.S. Census Bureau, ZUMA, Germany." (author's abstract)
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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 | 0.492 | 0.509 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.037 | 0.029 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".