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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".