Open science and Digital Commons for enabling reproducible, ethical and collaborative research
Bibliographic record
Abstract
This talk was delivered as a closing keynote at the Open Research Conference in Manchester on 24 April 2024. Versions of this talk: This talk was first created for the Closing Keynote for the Open Science Conference by Concordia University in Montreal in May 2022. This was then delivered as a deep dive talk for Genomics England in January 2023. It was improved and given as a closing keynote by Malvika Sharan on 10 March 2023 at the Digital Humanities in the Nordic and Baltic Countries conference DHNB2023 with the theme “Sustainability: Environment, Community, Data” organised by Annika Rockenberger, Senior Academic Librarian, Digital Research Methods in the Humanities and Social Sciences, University of Oslo Library along with colleagues from the University of Bergen Library and The Greenhouse Center for Environmental Humanities at the University of Stavanger. In September 2023, this talk was delivered as an opening keynote for the deRSE Unconference in Jena. It was then delivered as keynotes in October 2023 for the open access week in Derby and then co-delivered with Arielle Bennett for the Data Science Symposium in Denver, United States.
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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 | 0.092 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.034 | 0.039 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.061 | 0.031 |
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".