Framing a Situated and Inclusive Open Science: Emerging Lessons from the Open and Collaborative Science in Development Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.036 | 0.018 |
| Open science | 0.024 | 0.100 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it