Knowledge transfer after retirement: the role of corporate alumni networks
Bibliographic record
Abstract
Purpose The paper argues that organizations can use corporate alumni networks to capture and transfer the knowledge of baby boomers after the latter retire. Design/methodology/approach The paper introduces the concept of corporate alumni network and explains how this tool can facilitate post‐retirement knowledge transfer. Findings Corporate alumni networks enable organizations to recover the know‐how and know‐who of their retired employees in two ways. On the one hand, they help employees to preserve their personal relations with retired baby boomers. As a result, employees can rely on their retired colleagues for information and referrals in the same way that they do with other members of their informal networks. On the other hand, corporate alumni networks allow organizations to create a portfolio of working retirees who can be called up when necessary. Originality/value Although most organizations are aware of the need to preserve the in‐depth knowledge of soon‐to‐retire baby boomers, they focus mostly on pre‐retirement knowledge transfer activities. The paper expands the horizon by discussing a post‐retirement strategy.
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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 | 0.005 | 0.019 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".