Bibliographic record
Abstract
262Specific instance counts are based on the information provided in the Annual NCP Reports by 41 of the OECD Guideline adhering countries. Annual NCP Report is outstanding from Iceland. Not all NCPs report specific instances which have not been formally accepted. requests to consider specific instances have been raised with NCPs since the June 2000 review. Individual NCP reports indicate that the following numbers of specific instances have been raised: Argentina (7), Australia (4), Austria (5), Belgium (13), Brazil (22), Canada (11), Chile (6), Czech Republic (5), Denmark (3), Finland (4), France (18),France has had a significant increase in the number of specific instances it received in this implementation period. Six new specific instances have been raised in the past year as opposed to none in the previous five years. Germany (13), Hungary (1), Ireland (2), Israel (2), Italy (6), Japan (4), Korea (7), Luxembourg (3),Prior to this implementation period, Luxembourg had never received requests to consider specific instances. Mexico (3), Netherlands (21), New Zealand (2), Norway (6), Peru (3), Poland (3), Portugal (1), Romania (1), Spain (2), Sweden (3), Switzerland (16), Turkey (3), United Kingdom (24), and United States (32). 39 new specific instances were raised, more than double the number of specific instances raised in the 2009-2010 implementation period. A total of ten Final Statements, in addition to one revised Final Statement, were issued.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.362 | 0.226 |
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".