Framing a Situated and Inclusive Open Science: Emerging Lessons from the Open and Collaborative Science in Development Network
Bibliographic record
Abstract
What is open science and under what conditions could it contribute towards addressing persistent development challenges? How could we re-imagine and enrich open science so that it is inclusive of local realities and a diversity of knowledge traditions? These are some of the questions that the Open and Collaborative Science in Development Network (OCSDNet) is attempting to answer. In this paper, we provide the rationale and principles underlying OCSDnet, the conceptual and methodological frameworks guiding the research, and preliminary findings from the network's twelve globally diverse research projects. Instead of a “one-size-fits-all” approach to open science, our findings suggest that it is important to take into account the local dynamics and power structures that affect the ways in which individuals tend to collaborate (or not) within particular contexts. Despite the on-going resistance of powerful actors towards new forms of creating and sharing diverse knowledge, concluding evidence from the twelve research teams suggests that open science does indeed have an important role to play in facilitating inclusive collaboration and transformatory possibilities for development.
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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 | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.064 |
| Scholarly communication | 0.032 | 0.037 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".