SUPERIOR-SUBORDINATE KNOWLEDGE TRANSFER: WHAT SUPERIOR-SUBORDINATE KNOWLEDGE TRANSFER STRATEGIES ARE FAVORED BY HIGHLY EFFECTIVE GENERAL MANAGERS TO CREATE VALUE FOR THEIR ORGANIZATIONS?
Bibliographic record
Abstract
This qualitative study of 20 General Managers working in Canadian organizations found that understanding the nature of General Manager-subordinate knowledge transfer is critical to understanding how value-creating knowledge is acquired, created, transferred and applied in an organization. The following 10 characteristics of knowledge transfer strategies between GMs and their subordinates were identified: All of the GMs studied work in the presence of a strong information technology (IT) infrastructure; GMs believe that effective knowledge transfer creates value for organizations and their clients; GMs view value-creating knowledge transfer as part of a virtuous cycle: (as knowledge is brought to the cycle by the GM or a subordinate the total knowledge residing within the dyad increases, knowledge sharing within the dyad increases, knowledge creation is accelerated and the cycle continues); GMs treat knowledge as an asset that needs to be cultivated, managed, and shared; GMs treat value-creating knowledge as a corporate resource and not an individual one; GMs are clear about their knowledge transfer strategy; GMs link knowledge transfer to performance and provide incentives; GMs
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".