Research Impact, the ‘New Academic Capital’: An Environmental Scan of Research Impact Indicators and Resources for the Humanities and Social Sciences across 32 Countries
Bibliographic record
Abstract
Research impact agendas are gaining momentum globally and changing research policies from funding agencies and universities. This article reports on an environmental scan of research impact indicators and resources for Humanities and Social Sciences (HSS) from 32 countries. Changing policies from national research funders include new expectations for researchers to mobilize their research to non-academic audiences that could benefit from its use and demonstrate the tangible impacts of their work. Many have argued that research impact agendas disadvantage HSS as compared to Science, Technology, Engineering and Mathematics (STEM) fields. As such, the purpose of this environmental scan was threefold: (a) to examine how funding agencies are defining and conceptualizing research impact and KMb in different countries, (b) to gather and analyze research impact indicators used to assess HSS and (c) to identify practical resources that might support HSS researchers with research mobilization and impact. The scan yielded 721 research impact resources relevant to HSS; included analysis of 1,105 indicators; and identified 87 resources for researchers (including tools, networks, projects and open access repositories). Supplementary files for this article include a taxonomy of research impact indicators as well as a guidebook of research impact resources for researchers.
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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 | 0.032 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.056 | 0.175 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".