A systems‐based dynamic knowledge transfer capacity model
Bibliographic record
Abstract
Purpose The purpose of this paper is twofold: to understand how recent developments in systems thinking and social construction can influence understanding of knowledge transfer (KT); and to propose a new systems‐based knowledge transfer model. Design/methodology/approach The paper is a review of the literature on knowledge transfer, systems thinking and social construction leads to the proposal of a new KT paradigm. Findings The Dynamic Knowledge Transfer Capacity model (DKTC) found in this paper identifies the components required for social systems to generate, disseminate and use new knowledge to meet their needs. The model includes pre‐existing conditions, (need and prior knowledge) and four categories of capacities (generative, disseminative, absorptive and adaptive/responsive) that social systems must possess for KT to take place. Research limitations/implications The paper shows that the DKTC model is particularly well suited to analyzing complex systems with multiple stakeholders as opposed to small‐scale knowledge transfer systems. Empirical analysis in complex systems environments will help verify, enrich and generalize the model. Practical implications The paper sees that in an increasingly knowledge‐based economy, the ability to base decisions on the latest knowledge is vital for the success of organizations. The capacity for effective and sustained exchange between a system's stakeholders (researchers, government, practitioners, etc.); exchanges characterized by significant interactions reflected within the DKTC model, results in the appropriate use of the most recent discoveries in the decision making process. Originality/value The paper proposes a new knowledge transfer paradigm that views knowledge as a systemic, socially constructed, context‐specific representation of reality. The proposed knowledge transfer model is in sharp contrast to past attempts, focusing attention on the capacities that must be present in organizations and social systems as a precondition for knowledge transfer to occur.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".