Accountability, performance assessment, and evaluation: Policy pressures and responses from research councils
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
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.
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 | 0.225 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it