Sharing Knowledge between the Peers of a Winery Network: The Case of Wine Routes in Northern Greece
Bibliographic record
Abstract
It is widely accepted that effective knowledge management is an imperative factor for Destination Management Organizations and more especially Winery Networks. Although the importance of knowledge transfer has been acknowledged and thoroughly studied by scientists since the last quarter of the 20th century, tacit knowledge received less attention owing to the fact that it is very difficult to be codified and transferred. In this research paper we acknowledge the importance of knowledge transfer and we focus on the less attended and studied form of knowledge; the Tacit one, which according to many researchers is almost impossible to transfer. Towards this framework, we argue that tacit knowledge transfer could be facilitated through the use of customized rules and routines, based on Fuzzy Logic Rules. For that reason we contacted a research among wineries, in Northern Greece, examining the correlation extent among the factors that influence the knowledge transfer mechanism. The data extracted from the 37 interviews, well analyzed using the method of Factor Analysis and the most important subfactors were furthermore tested through multiple regression analysis method. The main findings of the research proved that fuzzy logic rules development will highly increase the levels of trust between the peers of the network.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".