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Record W2977935107 · doi:10.7202/1064654ar

Métamorphose technologique et institutions financières

2019· article· fr· W2977935107 on OpenAlexaffvenueabout
Marc Lacoursière, Ivan Tchotourian

Bibliographic record

VenueLes Cahiers de droit · 2019
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Le développement des nouvelles technologies est la source d’une ère novatrice pour le milieu des services bancaires et financiers. Le cloisonnement traditionnel entre le monde commercial et le milieu financier s’effrite, des institutions financières et des entreprises commerciales (jeunes, dynamiques et innovantes) offrant des services inédits de paiement, de crédit et d’investissement aux consommateurs. L’encadrement juridique des institutions financières qui assurait une certaine protection au consommateur est remis en question sous l’influence des entreprises de technologies financières (FinTech). Cela pose de nombreux problèmes juridiques. D’une part, le régulateur canadien se montre ouvert à accepter cette forme de concurrence, tant pour le système bancaire ouvert que dans le domaine des paiements, tout en conservant à l’esprit de bien protéger les intérêts des consommateurs. D’autre part, l’envahissement de l’intelligence artificielle dans le domaine de l’investissement amène le juriste à réfléchir sur la pertinence de l’encadrement réglementaire du conseil financier, notamment lorsque l’humain se trouve remplacé par l’automate. Devant des enjeux considérables, le Canada doit définir un encadrement qui place le consommateur dans une position lui permettant de profiter des possibilités des entreprises de technologie financière sans se placer pour autant dans une position risquée.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.005

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 head0.011
GPT teacher head0.222
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; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
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
Published2019
Admission routes3
Has abstractyes

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