Bibliographic record
Abstract
Knowledge is central to development. The \n World Bank invests about one-quarter of its budget for \n country services in knowledge products. Still, there is \n little research about the demand for these knowledge \n products and how internal knowledge flows affect their \n demand. About 49 percent of the World Bank's policy \n reports, which are published Economic and Sector Work or \n Technical Assistance reports, have the stated objective of \n informing the public debate or influencing the development \n community. This study uses information on downloads and \n citations to assesses whether policy reports meet this \n objective. About 13 percent of policy reports were \n downloaded at least 250 times while more than 31 percent of \n policy reports are never downloaded. Almost 87 percent of \n policy reports were never cited. More expensive, complex, \n multi-sector, core diagnostics reports on middle-income \n countries with larger populations tend to be downloaded more \n frequently. Multi-sector reports also tend to be cited more \n frequently. Internal knowledge sharing matters as cross \n support provided by the World Bank's Research \n Department consistently increases downloads and citations.
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 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 | 0.007 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.029 | 0.054 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.279 | 0.286 |
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".