Stories of Change: Covid-19 Responses for Equity
Bibliographic record
Abstract
Covid-19 Responses for Equity (CORE) was a three-year, CA$25m rapid research initiative that brought together 20 research projects to understand the socioeconomic impacts of the pandemic, improve existing responses, and generate better policy options for recovery. The research, funded by the Canadian International Development Research Centre (IDRC), took place across 42 countries in Africa, Asia, Latin America and the Middle East. The Institute of Development Studies (IDS) supported CORE to maximise the learning generated across the research portfolio and deepen engagement with governments, civil society, and the scientific community. This publication celebrates the impact of that research, and highlights Stories of Change from seven of the CORE projects that successfully influenced policy, practice, and understandings of the crisis. Collectively, these individual case studies provide a narrative about the nature of research impact in emergencies and the implications for the design and delivery of future rapid response research initiatives. There are clear lessons around the importance of organisational reputation, and the value of co-designing research with decision makers whilst simultaneously taking a critical position. Every story here emphasises the need to understand political context and to explore the trade-offs between research rigour and the timeliness of evidence. Above all, they illustrate the value of flexible funding arrangements that enable local teams to respond to fast-moving crises. These stories demonstrate unequivocally the value of locally led research responses to emergencies with the right international flow of resources and support. CORE’s research teams were well-placed to bring together communities, civil society organisations, and governments to create a space for vulnerable and marginalised groups to discuss their lived experiences of the pandemic and bring these perspectives into policy conversations. Their success hinged on their hyper-local knowledge and their unswerving focus on providing real-time evidence to advocate for the wellbeing of affected communities.
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.
How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.034 | 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; both teacher heads agree on what is shown here.
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".