How can impact strategies be developed that better support universities to address twenty-first-century challenges?
Bibliographic record
Abstract
To better address twenty-first-century challenges, research institutions often develop and publish research impact strategies, but as a tool, impact strategies are poorly understood. This study provides the first formal analysis of impact strategies from the UK, Canada, Australia, Denmark, New Zealand and Hong Kong, China, and from independent research institutes. Two types of strategy emerged. First, ‘achieving impact’ strategies tended to be bottom-up and co-productive, with a strong emphasis on partnerships and engagement, but they were more likely to target specific beneficiaries with structured implementation plans, use boundary organisations to co-produce research and impact, and recognise impact with less reliance on extrinsic incentives. Second, ‘enabling impact’ strategies were more top-down and incentive-driven, developed to build impact capacity and culture across an institution, faculty or centre, with a strong focus on partnerships and engagement, and they invested in dedicated impact teams and academic impact roles, supported by extrinsic incentives including promotion criteria. This typology offers a new way to categorise, analyse and understand research impact strategies, alongside insights that may be used by practitioners to guide the design of future strategies, considering the limitations of top-down, incentive-driven approaches versus more bottom-up, co-productive approaches.
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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 | 0.126 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.042 | 0.027 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".