Open science & development goals: shaping research questions
Bibliographic record
Abstract
What do we include in our definition of open science? And what is meant by development? Two key questions when you're discussing open science for development, as we were yesterday on day one of the IDRC OKFN-OpenUCT Open Science for Development workshop. Participants from Africa, Asia and Latin America and the Caribbean have gathered at the University of Cape Town in an attempt to map current open science activity in these regions, strengthen community linkages between actors and articulate a framework for a large-scale IDRC-funded research programme on open science. The scoping workshop aims to uncover research questions around how open approaches can contribute to development goals in different contexts in the global South. Contextualization of open approaches and the identification of their key similarities and differences is critical in helping us understand the needs and required frameworks of future research.
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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 | 0.154 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.017 | 0.076 |
| Scholarly communication | 0.045 | 0.049 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".