Towards a typology of knowledge-intensive organizations: determinant factors
Bibliographic record
Abstract
Phrases such as ‘knowledge-intensive organizations’ (KIOs) and ‘knowledge-intensive firms’ (KIFs), have recently found common usage, describing the distinct activities and attributes of some organizations. But a review of the literature reveals a lack of consensus among scholars and practitioners on the definition of KIOs. What is also absent from the discussion is an agreement on the factors that differentiate KIOs from non-KIOs, and how those factors affect knowledge management (KM) theory and practice. The objective of this paper is to extend a typology of KIOs as a preliminary step to conducting research on these types of organizations. With the typology of KIOs presented in this paper, we hope to provide a basis of distinguishing these organizations from other organizations, and also to allow one to perform comparative organizational analysis. The typology will also help researchers identify which of the organizations are knowledge-intense, and the nature of their knowledge-intensity, so that they help these organizations in designing appropriate KM tools.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".