Staff Perspectives on Scaling the Impact and Influence of MU Research and Expertise on Public Policy (Working Paper March, 2024. No. 20)
Bibliographic record
Abstract
Why this consultation, why now? Governments worldwide are calling upon higher education institutions (HEIs) to demonstrate more clearly their value to society as anchor institutions and the societal relevance and impact of their research, scholarship, and expertise. Many are using national research funding agencies to incentivise co-created research between academics and a wide range of beneficiaries. The Irish Government is no exception. Irish HEIs are being asked to step up and play their role in scoping impactful solutions to wicked and increasingly existential local, national, and global public problems. Of course, a significant body of work has already been undertaken or is in train. In this Science Foundation Ireland (SFI) (the Challenges, Public Service Fellowship and, Science Policy Research programmes); the Irish Research Council (IRC) (New Foundations and COALESCE programmes and ‘Roadmap on research for public policy’ (jointly with the Royal Irish Academic (RIA)); and The Irish University Association (Campus Engage programme) have led the way. But plans are afoot for the introduction of a new suite of interventions targeted at broadening and deepening linkages between academic researchers and policy-makers - to be layered on top of and to complement actually existing and already achieved knowledge exchange initiatives.
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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 | 0.145 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.041 | 0.022 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.023 | 0.016 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".