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Record W3121536166

Technological Progress, Organizational Change and the Size of the Human Resources Department ♣

2008· article· en· W3121536166 on OpenAlexaff
Patricia Crifo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Policies and Impacts
Canadian institutionsCenter for Interuniversity Research and Analysis on Organizations
Fundersnot available
KeywordsHuman capitalTechnological changeHuman resourcesHuman resource managementIndustrial organizationBusinessOrganizational changeHuman multitaskingKnowledge managementOperations managementLabour economicsEconomicsComputer scienceManagementPublic relationsEconomic growthMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

Mots clés: Les changements organisationnels reposant sur la polyvalence et les TIC qui se sont diffusés dans la plupart des pays de l'OCDE depuis les années 1990 ont de fortes conséquences sur les conditions de travail. Les données disponibles montrent, parallèlement à l'émergence de nouvelles formes organisationnelles fondées sur la polyvalence, une augmentation de la main d'oeuvre employée dans les postes managériaux et une augmentation des besoins en qualification. Cet article propose un modèle théorique analysant l'allocation optimale du nombre de tâches par individu lorsque le passage à une organisation fondée sur la polyvalence accroît les coûts de coordination entre les individus et les tâches. Les entreprises peuvent réduire ces coûts de coordination en affectant plus de salariés à la gestion des ressources humaines. Le capital humain est accumulé de manière endogène par les travailleurs. Le modèle reproduit assez bien les régularités observées dans les données. En particulier, des accélérations technologiques endogènes tendent à accroître à la fois le nombre de tâches réalisées et les besoins en qualification, tout en augmentant la part de la main d ' qui se consacre à la gestion des

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.211
Teacher spread0.161 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2008
Admission routes1
Has abstractyes

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