Think Global: Act Local - Ensuring an Equitable Transition to Open Science
Bibliographic record
Abstract
Shearer's presentation "Think Global: Act Local - Ensuring an Equitable Transition to Open Science" will focus on how open science promises to offer unprecedented access to the full corpus of research, breaking down access barriers for many researchers. However, there is a risk in the transition to open science - new barriers will be erected and a significant portion of researchers/authors will again be excluded from the system because of the predominance of pay to publish models. This presentation will examine the systemic factors including the transition to open science and discuss potential avenues for ensuring diversity, equity, and inclusivity across the scholarly publishing ecosystem. Shearer has been working in the area of open access, open science, scholarly communications, and research data management for over 20 years. She is the author of numerous publications and delivered many presentations at international events. Most recently, she was the lead author of the paper Fostering Bibliodiversity in Scholarly Communications: A Call for Action (April 2020). She participates in the work of numerous other organizations to advance open science around the world and is also a Research Associate with the Canadian Association of Research Libraries (CARL) and has been instrumental in many of CARL’s activities related to open science, including the launch of the Portage Initiative in Canada, a national research data management network.
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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 | 0.054 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.032 | 0.041 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.026 | 0.037 |
| Insufficient payload (model declined to judge) | 0.040 | 0.023 |
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".