Managing organizational memory with intergenerational knowledge transfer
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide the systematic analysis of an innovative, intergenerational knowledge transfer strategy in a knowledge‐intensive organization. Design/methodology/approach The case study method was adopted to study the intergenerational knowledge transfer activities. A triangulated approach was employed in respect of the data collection, which included non‐participatory observation, focus groups, documentary analysis, and semi‐structured interviews. A pattern analysis of data account was undertaken. Findings Two models for intergenerational knowledge transfer are presented: the source‐recipient model and the model of mutual exchange. This research also shows how a context conducive to knowledge transfer was developed, and concludes that this context allowed both explicit and tacit knowledge to be transferred. Research limitations/implications Often ignored or underestimated this study highlights the need for motivation, inspiration, and empowerment in knowledge transfer. The main limitation of this study is the generalizability of the findings. Practical implications The two models for intergenerational knowledge transfer provide a rubric against which both old and new intergenerational knowledge transfer initiatives can be assessed to determine whether they are capable of encouraging the transfer of both explicit and tacit knowledge. Originality/value There is little empirical work on the design and implementation of strategies for managing organizational memory. The integrated models and empirical results of this study can serve as guides in that process.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".