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Record W4323660771 · doi:10.3917/comma.192.0119

Des incivilités numériques dénoncées par les cadres

2023· article· fr· W4323660771 on OpenAlexaff
Delphine Dupré, Aurélie Laborde

Bibliographic record

VenueCommunication & management · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Les incivilités numériques font référence à des usages des technologies numériques qui transgressent les codes et les normes implicites de la vie en communauté et de la coopération au travail. Ce sont souvent des « micro-agressions », largement tolérées et diversement perçues par les salariés. Au-delà d’une simple question de courtoisie, elles rendent compte de transformations en cours des formes de communication en contexte de travail. Dans cet article, nous nous intéressons à la définition et la perception des incivilités numériques par une population de cadres intermédiaires. Nous montrons que ce que dénoncent les cadres, au-delà des incivilités numériques, sont des relations et une organisation du travail qu’ils réprouvent : communication « instrumentale », relation « client-fournisseur » en interne, dilution des frontières entre sphère professionnelle et privée, entre cœur de métier et pratiques périphériques, bureaucratisation des activités et perte de sens du travail. Les pratiques qui sont alors désignées comme inciviles sont révélatrices de dysfonctionnements organisationnels perçus par les cadres et rendent compte d’une distance critique vis-à-vis de l’« idéologie managériale ».

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)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0040.015
Scholarly communication0.0080.006
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.055
GPT teacher head0.311
Teacher spread0.256 · 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 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
Published2023
Admission routes1
Has abstractyes

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