Bibliographic record
Abstract
This paper reviews user-oriented official statistical services in light of developments in international debate and practices. Here, the concept of "users" includes the general public. The paper covers the following points. (1) The vision of user-oriented statistical services was formulated in the UN Fundamental Principles of Official Statistics, European Statistics Code of Practice, and similar documents. (2) Among the elements defining the quality of statistical data, relevance, clarity, interpretability, and accessibility constitute user-orientation. (3) The public release of information has significantly developed both in terms of from (microdata, metadata, and quality descriptions of the data) and the means of access (websites). (4) User satisfaction surveys have been carried out in many countries to identify needs. (5) Various channels have been exploited to encourage direct user-producer dialog. The paper examines the practices of the U.S. Bureau of the Census and Bureau of Labor Statistics, Statistics Canada, the Australian Bureau of Statistics, and especially SUF (Statistics Users Forum) in the UK. (6) It concludes by making suggestions based on the overview, nothing the necessity to innovate theoritical research on statistical production in the social statistics school in Japan and the necessity to enhance the user-orientation and quality management of Japanese official statistical services.
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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 | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".