Where does all the ‘<i>know how</i>’ go? The role of tacit knowledge in research impact
Bibliographic record
Abstract
Higher Education Institutions are increasingly called upon to demonstrate their real world impact, which, in many instances, remains elusive. We believe this is partly due to the under-counting and under-estimation of the importance of tacit knowledge by researchers and regulators. We propose this as a missing contingency in the research–impact relationship. To better acknowledge and utilize tacit research knowledge in the impact process, we emphasize processes of praxis, reflexivity and dialogical sense-making, which help externalize implicit tacit knowledge, and socialization processes, which facilitate enactment, emulation and feedback to develop inherent tacit knowledge. Examples from management research are used to exemplify these processes. The implications of accepting the importance of tacit knowledge in creating impact call for changes in how researchers, universities, funders, assessors and governments, fund, create and assess real world research impact.
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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 | 0.140 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.073 |
| Scholarly communication | 0.044 | 0.054 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".