Bibliographic record
Abstract
Knowledge management is often subjected to dichotomised thinking. For example, distinctions are made between tacit and explicit knowledge (Nonaka 1991), individual and shared knowledge (Moingean and Edmondson 1996), and codification and personalisation processes of knowledge management (Hansen et al. 1999). Underlying these conceptual distinctions is the established theory of Argyris and Schön (1974, 1978) on single loop (‘the how’), and double loop, (‘the why’) of learning. Although Argyris and Schön (1974) were careful to avoid over-simple characterisations, a view of knowledge as content (information, facts and data) plus optional processes-such as the interactive and social elements of learning (Brockbank and McGill 1998)—has persisted in having an impact on practice, particularly where ‘codification’ and ‘transfer’ (Bassi 1997) are the foci. In this chapter we seek to challenge some of the dichotomised thinking around knowledge management and argue for a view which incorporates rationality and emotion, openness and secrecy, physicality and virtualness, and anticipation and reflection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.112 | 0.036 |
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".