Towards a typology of knowledge-intensive organizations: determinant factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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 it