Mutual learning for knowledge co-creation about disability inclusive development programmes and practice
Bibliographic record
Abstract
To deal with world-wide problems in development, actors need to co-create new knowledge. This can be done through mutual reflection on underlying values and assumptions and by combining the knowledge of different actors from society and academia. This paper shows how knowledge co-creation can be facilitated with attention to multiple actor collaboration, creating outputs which are relevant for science and society and which contribute to sustainable development. We describe how a group of different actors can become mutually engaged to co-create knowledge in a shared domain of interest. Through mutual learning and experimentation in a community of practice, the actors develop a shared repertoire of socially robust knowledge. The balance between theory and practice during knowledge co-creation process helped to gain in-depth understanding of the process. This shows the importance of mutual learning and co-creation of knowledge when a new issue is introduced in development practices. To illustrate this, the experiences of over 30 organisations, united in a community of practice on disability inclusive development, are considered.
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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 | 0.034 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.003 | 0.035 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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".