Individual knowledge measurement: organizational knowledge measured at the individual level
Bibliographic record
Abstract
Purpose Fundamental classifications of knowledge may be measurable as factors of production and can reveal evidence of specialization between adjacent stages of production even in the presence of shared substantive knowledge. This study of aims to distinguish between, and empirically measure, relative reliance on fundamental classifications of knowledge at the individual level. Design/methodology/approach In this study, investment managers were asked in an online survey to weigh their relative reliance on tacit, codified and encapsulated knowledge in executing different investment strategies for diverse client groups. Measures of relative reliance on each fundamental classification of knowledge were derived from weights assigned by each survey respondent in a series of six questions. Findings Survey respondents provided reliable measures of their relative reliance on tacit, codified and encapsulated knowledge. Reliance on these fundamental classifications of knowledge is shown to differ between investment managers, depending on the investment strategies being used and client groups served. These differences were exhibited notwithstanding all the respondents sharing common substantive knowledge. Research limitations/implications Measures of relative reliance on three classifications of knowledge were based on self-reported ratings rather than on objectively observed phenomena, making them subject to measurement error. Therefore, researchers are encouraged to observe relative reliance on tacit, codified and encapsulated knowledge in future studies. Originality/value The divergences in relative reliance on the fundamentally different knowledge-based factors of production were found in the presence of jointly held substantive knowledge, suggesting that fundamental classifications of knowledge are measurable and can provide evidence of specialization.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".