The neglect of equity and inclusion in open science policies of Europe and the Americas
Bibliographic record
Abstract
National, international, and organizational Open Science (OS) policies are being formulated to improve and accelerate research through increased transparency, collaboration, and better access to scientific knowledge. Yet, there is mounting concern that OS policies—which are predicated on narrow understandings of openness, accessibility, and objectivity—do not effectively capture the ethos of OS and particularly its goal of making science more collaborative, inclusive, and socially engaged. This study explores how OS is conceptualized in emerging OS policies and to what extent notions of equity, diversity, and inclusion (EDI) and public participation are reflected in policy guidelines and recommendations. We use a qualitative document research approach to critically analyze 52 OS policy documents published between January 2020 and December 2022 in Europe and the Americas. Our results show that OS policies overwhelmingly focus on making research outputs publicly accessible, neglecting to advance the two aspects of OS that hold the key to achieving an inclusive and inclusive scientific culture—namely, EDI and public participation. While these concepts are often mentioned and even embraced in OS policy documents, concrete guidance on how they can be promoted in practice is overwhelmingly lacking. Rather than advancing the openness of scientific findings first and promoting EDI and public participation efforts second, we argue that incentives and guidelines must be provided and implemented concurrently to advance the OS movement's stated goal of making science open to all.
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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 | 0.075 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".