Knowledge in Times of Crisis: Transforming Research-to-Policy Approaches
Bibliographic record
Abstract
The Covid-19 pandemic resulted in unprecedented challenges for researchers and policy analysts, and accentuated the need for access to civil society and advocacy movements within politically closed spaces. The impact of locally led Covid-19 response research in the global South has subsequently raised questions about traditional research methods that often prioritise academic rigour over practical relevance and result in research disconnected from the realities of people’s lives. \nThis issue of the IDS Bulletin presents learning gathered from rapidly mobilised Southern-led research by institutions who designed and delivered research aimed at influencing the response to the Covid-19 pandemic. The articles here are drawn from the Covid-19 Responses for Equity (CORE) programme, a rapid research initiative created to understand the socioeconomic impacts of the pandemic in order to generate better policy for recovery. \nThe IDS Bulletin explores the particular characteristics of Southern research organisations that were able to mobilise quickly. It discusses the types of knowledge that were needed in these unique circumstances, and how organisations mobilised knowledge in an emergency to facilitate engagement and influence response to a global challenge with local implications. \n \nThe examples here demonstrate how researchers have developed new skills to present research findings in accessible ways for different audiences. The explicit use of digital technologies, for instance, has allowed researchers to facilitate collaboration across geographic boundaries and engage diverse stakeholders. \n \nThis all highlights how essential locally led research is for pandemic response and for development more broadly. There is also acknowledgement that how organisations responded to the pandemic may have a longer-term impact on the future of those organisations themselves.
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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 | 0.114 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.015 | 0.156 |
| Scholarly communication | 0.052 | 0.080 |
| Open science | 0.010 | 0.043 |
| Research integrity | 0.027 | 0.026 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".