Trouble with tacit: developing a new perspective and approach
Bibliographic record
Abstract
Purpose – The purpose of this viewpoint paper is to question the widely adopted tacit-explicit distinction of knowledge, arguing that this is based on a misappraisal of the original source of the “tacit” phenomenon. Design/methodology/approach – It is argued that Michael Polanyi’s theory of personal knowledge and philosophical grounds have been misinterpreted. The tacit problem is approached from three different directions: knowledge management, cognitive psychology and discursive psychology. The first offers an imperative to regard the tacit as vital to organizational success and an underplayed “implicit” perspective on the tacit. The second offers empirical evidence for the formulation of the tacit as acquired automatically and unconsciously through implicit learning and as influencing action. The last offers a theory and methodology for studying what is argued as being the primary site of knowledge work – discourse. Findings – A novel aspect of the tacit – “tacit knowing” – is shown to be action-orientated and influential, and while it is a hidden aspect of a person’s knowledge, it can be revealed through the study and analysis of discourse. Originality/value – This is the first known paper in the extant literature to examine the tacit knowledge challenge from these combined directions. Implications for practice and study are discussed, and new directions for research proposed.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.004 | 0.008 |
| 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".