Bibliographic record
Abstract
Statistical organizations disseminate statistics to an extent never seen before. However according to in-depth analysis of user needs, it is an urgent task to give end-users better assistance when they use statistics or wish to find relevant statistics. The paper suggests that metadata should be defined and implemented targeted at providing help to end-users. Statistics Denmark strives for an integrated metadata approach with quality-, concepts-, variables- and classification-elements. To fulfil this, standards and tools used in Sweden, Portugal, Canada etc. were investigated. It was apparent that all solutions could be used to fulfill our needs, but still a lot of work on development was required. In 2011 DDI-L was discovered. The standard looked like as an excellent way forward. Since then Statistics Denmark, together with Danish Data Archive as reviewer, has carried out a pilot-project and other tests of the standard using various software implementations - Colectica being the main tool. The paper will present models, DDI-L structures and issues and thereby contribute to a common understanding of the use of DDI-L in the production of official statics that would benefit both the international DDI-community and the international statistical community. We hope this paper can contribute to this process.
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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 | 0.176 | 0.319 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.027 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.029 | 0.020 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.015 |
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".