Lessons Learned from Mobilising Research for Impact During the Covid-19 Pandemic
Bibliographic record
Abstract
During the Covid-19 pandemic, research organisations have strived to be resilient. This means navigating through the technical, operational, and political challenges to achieving successful research implementation. Particularly for local policy research thinktanks, the pandemic has made these challenges even more difficult to address. From the experience of the Cambodia Development Resource Institute (CDRI) in implementing large-sample research in the formal and informal sectors during the pandemic, these challenges are countered through: (1) the incorporation of a technical advisory team; (2) the adoption of a flexible resource allocation strategy; and (3) the implementation of a quality assurance system. Policy research is only impactful when the knowledge produced serves its purpose as evidence to inform policymaking and guide programme intervention. To realise this objective, CDRI implements three types of engagement activities (consultation, coordination, and validation) that provide opportunities for interaction between researchers and relevant stakeholders.
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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 | 0.117 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".