Social Science Question Database and Research Tools
Bibliographic record
Abstract
The Réseau Quetelet, French data archives for social science, has developed a Social Science Question Database and Research Tools that allows users to search for questions (question texts, answer texts, variable labels) across datasets, compare results, and save them. The analysis can be extended to the roots of words or to include stop words. Information on each question include: questions text, categories of answer, location of the variable in the dataset, link to the variables before and after, instructions given to interviewers, text before and after the question, universe of the question, links to questionnaires. Users can store questions and export them (csv or xls). The question database is based on DDI (version 2) and the research module on Apache Solr.// The paper was presented at the CESSDA Expert Seminar, Université de Lausanne, 20 octobre 2011 / 4th conference of the European Survey Research Association, Lausanne, Suisse, 18-22 juillet 2011 / 37th conference of IASSIST, Vancouver, Canada, 30 mai-3 juin 2011 / Conférence NTTS (New Techniques and Technologies for Statistics) organisée par Eurostat, Bruxelles, Belgique, 22-24 février 2011 / Journée du Réseau Quetelet sur les bases de questions, Sciences Po, Paris, 28 janvier 2011
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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 | 0.043 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.085 | 0.034 |
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".