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Record W7001624361

Les opérations scrutées sous l'angle de l'intérêt public par les organismes de réglementation des valeurs mobilières : entre efficience et duplicité

2008· other· fr· W7001624361 on OpenAlexvenueno aff

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2008
Typeother
Languagefr
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
Fundersnot available
KeywordsVulnerability (computing)PilotageCriminal liabilityDecantation
DOInot available

Abstract

fetched live from OpenAlex

Les organismes de réglementation des valeurs mobilières doivent veiller à la\nprotection des investisseurs et à l'efficience des marchés financiers. Pour ce faire, ils\nont été investis de larges pouvoirs discrétionnaires leur permettant de contrôler les\nopérations qui, quoique conformes aux prescriptions légales, sont susceptibles de porter\natteinte à l'intérêt public.\nLa notion d'intérêt public étant malléable et difficile à circonscrire, la discrétion\nconférée à ces organismes est étendue. Son exercice pouvant menacer la sécurité\njuridique des opérations et être associé à l'arbitraire, elle a été décriée par de nombreux\nobservateurs qui ont demandé qu'elle soit limitée aux cas de transgression de la\nlégislation sur les valeurs mobilières.\nLe texte cherche à déterminer si les organismes de réglementation des valeurs\nmobilières ont fait preuve de l'ingérence appréhendée par certains en recourant à leur\ndiscrétion relative à l'intérêt public. Tout en faisant ressortir la portée large et flexible\nde cette discrétion, il cherche à cerner les types d'opérations susceptibles d'en entraîner\nl'exercice. Bien qu'il ne propose pas une refonte du cadre juridique actuel, il soulève\ndes points de réflexion et suggère des avenues qui pourraient être envisagées afin de\nlimiter les risques y étant associés.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.029
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0090.006
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0290.006

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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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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Same venueLibrary and Archives Canada (Government of Canada)Same topicMachine Learning in BioinformaticsFrench-language works237,207