Bibliographic record
Abstract
Abstract: This article addresses ways to enhance the quality of evaluations with weak designs through a variety of quality assurance practices. Many types of evaluations are restricted in the types of designs they can use. Evaluations of development programs with widely dispersed projects in different countries are often a case in point, where the design uses visits to a number of dispersed sites, interviews with staff and stakeholders, and reviews of documentation to draw conclusions. These interview-based evaluations are quite similar in methodological approach to many performance audits. National audit offices devote considerable resources to their quality assurance practices, and, for the most part, the quality of their performance audits is not questioned. It is argued that evaluations, and not only interview-based ones, could usefully adopt many of the quality assurance practices used by national audit offices to ensure the quality of their products.
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 | 0.559 | 0.623 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".