Research and Development in Official Statistics and Scientific Co-operation with Universities: A Follow-Up Study
Bibliographic record
Abstract
This article summarizes the main results of a follow-up survey of National Statistical Institutes concerning two related aspects: (a) Research and Development (R&D) work within an agency, and (b) scientific co-operation of a National Statistical Institute with the universities. The initial survey was carried out in 1999/2000 and the follow-up in 2006. We concentrated for the aspect (a) on the infrastructure available for R&D within an agency, and for (b) on networking and similar co-operation arrangements of National Statistical Institutes with universities. The levels of R&D infrastructure and of R&D networking were measured by means of summary indicators constructed from the questionnaire items. Both indicators show that a large variation exists between National Statistical Institutes (and groups of such institutes). A high level of infrastructure often accompanied a high level of networking. When both levels were high, the chances of a successful implementation of research results into the production of statistics were improved. However, the incidence of successful implementation is lower than desirable. In National Statistical Institutes of European Union countries, the levels of both infrastructure and networking were improved between the survey years. The results of the 2006 survey show an increasing use of the agency’s anonymized microdata files by researchers located outside the agency. This was found to hold for the National Statistical Institutes of the EU countries in particular. A total of 41 agencies (80%) responded to the 2000 survey and 44 agencies (85%) to the 2006 survey.
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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 | 0.020 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".