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Record W2140055463 · doi:10.1108/13673270810859569

Qualified ageing workers in the knowledge management process of high‐tech businesses

2008· article· en· W2140055463 on OpenAlexaffabout
Mehran Ebrahimi, Anne‐Laure Saives, W. David Holford

Bibliographic record

VenueJournal of Knowledge Management · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsHEC MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsKnowledge managementOriginalityThematic analysisJudgementPersonal knowledge managementProcess (computing)Human resource managementKnowledge value chainBusinessOrganizational learningComputer scienceSociologyQualitative researchPolitical science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.032
GPT teacher head0.275
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2008
Admission routes2
Has abstractyes

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