Qualified ageing workers in the knowledge management process of high‐tech businesses
Bibliographic record
Abstract
Purpose The purpose of this paper is to aim to characterise the knowledge management process of the ageing human capital, within the sectors of aeronautics and bio‐technologies in Canada. Design/methodology/approach The methodology consists of: cross‐search of literature towards the elaboration of a theoretical map; and collection of data involving semi‐directed interviews followed by a thematic and statistical analysis of the textual data. Findings Management's knowledge of social and relational knowledge, especially those of ageing workers, appears to be scarce, thus resulting in ageing workers being perceived as surpassed by technological and scientific progress. This conception deprives the company of an important source of knowledge capitalisation. A model relevant to the evaluation of company practices related to inter‐generational aspects of knowledge management should include six basic dimensions, namely: management philosophy (a managerial style favouring projections and proximities), strategic analysis (knowledge, memory and learning strategy), organisational analysis (information management system and knowledge creation process), operational analysis (places of socialisation), competencies (relational and communicational know‐how, individual memory and capacity of judgement), and the role of ageing personnel (activation of organisational and human resource networks). Research limitations/implications Further validation is required across an enlarged population, with the aim of operationalising the observed concepts within a practical evaluation guide of company practices related to inter‐generational aspects of knowledge management. Originality/value By centering the analysis on highly qualified ageing individuals, the authors discerned a phenomenon showing that even within highly technological contexts knowledge management is far from systematically integrating those recognised a priori as carriers of knowledge.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".