Lessons Learned from Mobilising Research for Impact During the Covid-19 Pandemic
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
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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