Knowledge communities: towards a re‐thinking of intergenerational knowledge transfer
Bibliographic record
Abstract
Purpose Knowledge management (KM) has become a key concern for companies which nowadays are constantly looking for better ways to assure knowledge sharing between their employees. However, companies encounter several challenges arising from the fact that several generations share the same workplace and a big portion of today's employees are close to retirement. This article aims to focus on knowledge sharing between generations. Design/methodology/approach The article reviews the “generation” concept and its limitations, and introduces a new view on generations as “communities of knowledge”. Findings Companies have to find ways not only to assure knowledge transfer between generations, but also knowledge retention of the workers that are retiring. This requires a deeper understanding of the generations and their differentiated knowledge. Yet, today's dominant descriptions of generations (“baby‐boomers”, “generation X”, “generation Y”), do not appear to adequately take into account cultural, socio‐professional and individual factors. Originality/value The proposed change of paradigm allows a deeper comprehension of nuances that may exist within the same age group. In doing so, the article makes a contribution to the understanding of knowledge sharing in organizations.
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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.013 | 0.031 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| 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".