Knowledge transfer after retirement: the role of corporate alumni networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it