Decolonising Knowledge for Development in the Covid-19 Era
Bibliographic record
Abstract
This Working Paper seeks to explore current and emerging framings of decolonising knowledge for development. It does this with the intent of helping to better understand the importance of diverse voices, knowledges, and perspectives in an emerging agenda for development research. It aims to offer conceptual ideas and practical lessons on how to engage with more diverse voices and perspectives in understanding and addressing the impacts of Covid-19. The authors situate their thoughts and reflections around experiences recently shared by participants in international dialogues that include the Covid Collective; an international network of practitioners working in development contexts; engagement and dialogue with Community-based Research Canada, and their work with the Victoria Forum. Through these stories and reflections, they bring together key themes, tensions, and insights on the decolonisation of knowledge for development in the context of the Covid-19 era as well as offering some potential ways forward for individuals and organisations to transform current knowledge inequities and power asymmetries. These pathways, among other solutions identified, call for the inclusion of those whose challenges are being addressed, reflective spaces for inclusive processes, and connection, sharing and demonstrating the value of decolonised knowledge for liberation and trust.
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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 | 0.037 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.082 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.003 | 0.036 |
| Research integrity | 0.004 | 0.009 |
| 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".