Bibliographic record
Abstract
This is a presentation at the online conference Open Research: A Vision for the Future, hosted by the RIOT Science Club, King's College London. http://riotscience.co.uk/open-research-a-vision-for-the-future/ Debates about the how and why of Open Science have tended to focus on the technicality, standards, and conditions about what is and what isn’t “open”. More importantly, the guidelines and principles on open science that have been proliferating are centered on largely Western and Global North perspectives. The more crucial questions of by whom and for whom should science be open, and who has the power to set the agenda of open science are often not addressed. In this talk, I like to highlight some of the values and benefits of openness to knowledges and ways of knowing from communities and knowledge makers who have been historically excluded from “main-stream science.” I like to share ideas on how a pluriversal open science commons based on epistemic justice principles and solidarity, drawn from Indigenous and other knowledge traditions, can be sustained and governed by communities and for communities in various contexts.
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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 | 0.072 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.061 |
| Scholarly communication | 0.036 | 0.065 |
| Open science | 0.004 | 0.038 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".