Informal knowledge sharing behavior of scientists: evidence of exchanging and withholding technological information
Bibliographic record
Abstract
This study explores the relationship between reputation and knowledge sharing behavior in an environment where R&D work is embedded in a social context and may be broken down physically, organizationally, and by area of technology. Scientists in the same firm, though comprising a social community, may work in different locations, may be separated by organizational boundaries (e.g., teams, departments), and work in different scientific disciplines. Findings illuminate attributes of reputation conducive to collaboration. We examine one scientist's decision to provide or not provide technological knowledge to another scientist in his or her firm. Analysis is based on 213 returned surveys. We found the dimensions of reputation, past behavior and expected action, influence the knowledge sharing decision. I. INTRODUCTION Though many firms have adopted policies and programs to encourage the collection, storage, and dissemination of codified knowledge that resides within the firm, less certain is how to foster the flow of knowledge that resides in individuals. One item that may facilitate or retard such collaboration - the voluntary sharing of non-codified knowledge - is the potential recipient's reputation. The decision to assist a fellow employee with their work is based in part upon the signal that his or her reputation sends. We engaged a debate on the role social governance plays in the exchange of resources. We examine the relationship between reputation and the voluntary sharing of technological
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".