Co-Constructing an Open and Collaborative Manifesto to Reclaim the Open Science Narrative
Bibliographic record
Abstract
The OCSDNet Manifesto is a result of one year of participatory consultations and debates amongst members of the ‘Open and Collaborative Science in Development Network’ (OCSDNet), a network of 12 research-practitioner teams from Latin America, Africa, the Middle East and Asia. Through research projects grounded in diverse regions and disciplines, OCSDNet members explore the scope of Open Science as a transformative tool for development thinking and practice and offer the ‘Open and Collaborative Science Manifesto’ as a foundation upon which to reclaim the mainstream narrative about what Open Science means and how it can realise a more inclusive science in development. This paper describes the mechanisms used for collaboration and consensus building, and explores the ways in which the process of building this document serves as a case study for the opportunities and limitations of integrating collaboration, opportunities for participation and openness into research activities.
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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 | 0.053 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".