Decolonising Knowledge for Development in the Covid-19 Era
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
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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