Improving Reproducibility in Research: The Role of Measurement Science
Bibliographic record
Abstract
We report on a workshop held 1-3 May 2018 at the National Physical Laboratory, Teddington, U.K., in which the focus was how the world's national metrology institutes might help to address the challenges of reproducibility of research.The workshop brought together experts from the measurement and wider research communities in physical sciences, data analytics, life sciences, engineering, and geological science. The workshop involved 63 participants from metrology laboratories (38), academia (16), industry (5), funding agencies (2), and publishers (2). The participants came from the U.K., the United States, Korea, France, Germany, Australia, Bosnia and Herzegovina, Canada, Turkey, and Singapore.Topics explored how good measurement practice and principles could foster confidence in research findings and how to manage the challenges of increasing volume of data in both industry and research.
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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 | 0.858 | 0.929 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.006 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.009 | 0.079 |
| Scholarly communication | 0.036 | 0.048 |
| Open science | 0.011 | 0.027 |
| Research integrity | 0.014 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".