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Record W2029853165 · doi:10.1108/09593840110411167

The impact of knowledge repositories on power and control in the workplace

2001· article· en· W2029853165 on OpenAlexaff
Peter Gray

Bibliographic record

VenueInformation Technology and People · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsQueen's University
Fundersnot available
KeywordsKnowledge managementControl (management)Work (physics)Principal (computer security)Position (finance)Knowledge value chainPower (physics)BusinessKnowledge workerDistribution (mathematics)Personal knowledge managementInformation technologyComputer scienceOrganizational learningEngineeringComputer security

Abstract

fetched live from OpenAlex

Knowledge management systems designed to facilitate the storage and distribution of codified knowledge affect the distribution of power within organizations. Drawing on the literature that describes the impact of information technology on power and control, this article proposes two principal outcomes of the implementation and use of such knowledge repositories. The use of knowledge repositories by employees who are net re‐users of knowledge‐based work products is expected to increase the extent to which these employees are interchangeable while reducing the level of skill they need to carry out their work. For employees who are net contributors to knowledge‐based work products, the use of knowledge repositories produces the opposite effect. When managers choose to capitalize on these effects to increase their control, employees in the former group may find their power position eroded over time.

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)0.006
metaresearch head score (Gemma)0.026
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.008
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.278
Teacher spread0.272 · 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

Citations179
Published2001
Admission routes1
Has abstractyes

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