Index
Bibliographic record
Abstract
As international interest in promoting and assessing the impact of research grows, this book examines the ensuing controversies, consequences and challenges. It places a particular emphasis on learning from experiences in the UK, since this is the country at the forefront of a range of new approaches to incentivising, monitoring and rewarding research impact achievements. The book aims to understand the origins and rationale for these changes and to critically assess their consequences for academic practice. Combining a review of existing literature with a range of new qualitative data (from interviews, focus groups and documentary analysis), The Impact Agenda is unique in providing a comprehensive, cross-disciplinary empirical examination of the ways in which various forms of research impact assessment are shaping academic practices. Although the primary focus of the book is on the UK, the book also considers the different approaches that other countries with an interest in research impact are taking (notably Australia, Canada and the Netherlands). While noting the benefits that the increasing emphasis on outward facing work is bringing, the book draws attention to a wide range of challenges and controversies associated with research impact assessment and, in particular, with the UK’s chosen approach. It concludes by using the insights in the book to propose an alternative, more theoretically robust approach to incentivising and rewarding efforts to undertake and use academic research for societal benefit.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.572 | 0.521 |
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".