Where does all the ‘<i>know how</i>’ go? The role of tacit knowledge in research impact
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it