Bibliographic record
Abstract
Purpose This research seeks to respond to Simon's challenge to apply “an economic calculus to knowledge”. The paper aims to develop a typology of knowledge that may be fruitful in facilitating research in a knowledge‐based view of production. Design/methodology/approach The paper reviews the enduring literature on the knowledge‐based view of the firm (KBV) and gleans three classifications of organisational knowledge as distinct factors of production: tacit, codified, and encapsulated knowledge. Findings Differences between the tacit, codified, and encapsulated shapes of knowledge carry strategic implications for the firm along six important dimensions. Distinguishing between its three classifications sets the stage for measurement of knowledge as a factor of production. Research limitations/implications Distinctions between the three shapes of knowledge may be less defined in practice than in theory. The classification in which a repository of knowledge falls is dependent on the tacit knowledge being applied by the user. Software may be encapsulated to a user, but codified to its creator. Practical implications Recognition of the differences between the three shapes of organisational knowledge may help managers to: determine the most economic combination of knowledge to use in production; transfer knowledge more effectively within and across organisational boundaries; determine the most economic location of firm boundaries; and ensure value is appropriated for the firm. Originality/value The paper suggests that distinguishing and accentuating encapsulated knowledge as a distinct classification of knowledge can help advance the development of a strategic knowledge‐based theory of production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".