Bibliographic record
Abstract
This chapter reviews recent literature on knowledge and knowledge transfer (KT) and proposes the emergence of a classification system of the core KT concepts, models, and contexts that helps address issues of a strategic nature. The two paradigms that inform most of the KT literature, the positivist and social construction paradigms, and their implications on strategy formulation, are discussed. The positivist paradigm views knowledge as an object that can be passed on mechanistically from the creator to a translator who then adapts and transmits it to the user. The social construction paradigm views knowledge as the dynamic by-product of interactions between human actors who are trying to understand, name, and act on reality. In keeping with this dual paradigm logic, the literature on KT can be categorized as originating either from an information technology paradigm or an organic paradigm. The chapter discusses how most of the past strategy-related KT issues focused on the transfer of explicit knowledge and indicates that the future direction implies a shift in attention towards more tacit knowledge transfer considerations.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.027 |
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".