Accountability, performance assessment, and evaluation: Policy pressures and responses from research councils
Bibliographic record
Abstract
This study identifies contemporary government accountability requirements impacting research councils in North America and Europe and investigates how councils deal with such demands. This investigation is set against the background of rising policy frameworks stressing public sector accountability that have led many national governments to enact legislation requiring public agencies to collect more performance information and tie it to decision-making. Through documentary analysis and interviews with informants at several research councils we clarify how broader policy trends are reflected in the operation of public institutions that provide critical support for academic science. In addition to legislation cast broadly to regulate the activities of all government agencies, numerous regulations and guidelines have been targeted specifically at science and technology (S&T) activities. Regulations on S&T expenditures in general and on research councils more specifically include efforts to develop new metrics specific to science-based or innovation-based outcomes, to enhance the use of indicators in decision-making, to focus on tracing the broad impacts of programs, to increase the frequency of reporting, and to make agencies more responsive to business and public interests.
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 | 0.491 | 0.586 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.026 | 0.034 |
| Scholarly communication | 0.033 | 0.018 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.021 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".