Insights for Influence: Understanding Impact Pathways in Crisis Response
Bibliographic record
Abstract
The Covid-19 Responses for Equity (CORE) programme was a three-year initiative funded by the Canadian International Development Research Centre (IDRC) that brought together 20 projects from across the global South to understand the socioeconomic impacts of the Covid-19 pandemic, improve existing responses, and generate better policy options for recovery. The research covered 42 countries across Africa, Asia, Latin America, and the Middle East to understand the ways in which the pandemic affected the most vulnerable people and regions, and deepened existing vulnerabilities. Research projects covered a broad range of themes, including macroeconomic policies for support and recovery; supporting essential economic activity and protecting informal businesses, small producers, and women workers; and promoting democratic governance to strengthen accountability, social inclusion, and civil engagement. The Institute of Development Studies (IDS) provided knowledge translation (KT) support to CORE research partners to maximise the learning generated across the research portfolio and deepen engagement with governments, civil society, and the scientific community. As part of this support, the IDS KT team worked with CORE project teams to reconstruct and reflect on their impact pathways to facilitate South-South knowledge exchange on effective strategies for research impact, and share learning on how the CORE cohort has influenced policy and delivered change. This report presents an overview of these impact pathways and the lessons learnt from a selection of the projects chosen to represent the diversity of approaches to engage policymakers, civil society, and the media to generate and share evidence of the effect of the pandemic on diverse vulnerable groups.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".