Open Science Beyond Open Access: For and with communities, A step towards the decolonization of knowledge
Bibliographic record
Abstract
UNESCO is launching international consultations aimed at developing a Recommendation on Open Science for adoption by member states in 2021. Its Recommendation will include a common definition, a shared set of values, and proposals for action. At the invitation of the Canadian Commission for UNESCO, this paper aims to contribute to the consultation process by answering questions such as: • Why and how should science be “open”? For and with whom? • Is it simply a matter of making scientific articles and data fully available to researchers around the world at the time of publication, so they do not miss important results that could contribute to or accelerate their work? • Could this openness also enable citizens around the world to contribute to science with their capacities and expertise, such as through citizen science or participatory action research projects? • Does science that is truly open include a plurality of ways of knowing, including those of Indigenous cultures, Global South cultures, and other excluded, marginalized groups in the Global North? The paper has four sections: “Open Science and the pandemic” introduces and explores different forms of openness during a crisis where science suddenly seems essential to the well-being of all. The next three sections explain the main dimensions of three forms of scientific openness: openness to publications and data, openness to society, and openness to excluded knowledges2 and epistemologies3. We conclude with policy considerations. A French version of this paper is available here: https://zenodo.org/record/3947013#.Xw-Ksx17nOQ
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.118 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.024 | 0.161 |
| Scholarly communication | 0.045 | 0.100 |
| Open science | 0.006 | 0.074 |
| Research integrity | 0.027 | 0.033 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".