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Record W2914036719 · doi:10.1522/radm.no1.917

L’ ubérisation des services financiers, une tendance lourde

2018· article· fr· W2914036719 on OpenAlexaffvenue
Richard-Marc Lacasse, Berthe Lambert

Bibliographic record

VenueAd machina l avenir de l humain au travail · 2018
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

L’avènement de l’Internet des objets connectés a causé dans le monde des services financiers une véritable révolution numérique. Cet article traite de la rupture des pratiques à la suite de l’avènement des plateformes numériques intelligentes dans le secteur des services financiers. En première partie, l’origine et la pertinence du phénomène sont explorées, le tout suivi d’un examen de l’état d’esprit d’un banquier traditionnel relativement aux nouveaux modèles d’affaires en train de perturber l’emploi. L’article se termine sur le désarroi des autorités réglementaires face au phénomène. Nos données prospectivistes fournissent les bases conceptuelles sur lesquelles sont fondés les nouveaux modèles d’affaires dans le secteur des services financiers. Les concepts sont originaux et intuitifs, mais basés sur des observations empiriques, sur les rapports du Forum économique mondial de Davos et sur l’analyse de nouvelles licornes dans la niche des services financiers. Le principal constat de l’article : nous sommes en présence d’un tsunami numérique qui fera disparaître des millions d’emplois dans le secteur des services financiers traditionnels.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.018
GPT teacher head0.230
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2018
Admission routes2
Has abstractyes

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