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Record W1990762100 · doi:10.1108/13590790810882856

Measuring market integrity: a proposed Canadian approach

2008· article· en· W1990762100 on OpenAlexaffabout
Bryan Fodor

Bibliographic record

VenueJournal of Financial Crime · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsRoyal Canadian Mounted Police
Fundersnot available
KeywordsOriginalityPerspective (graphical)EnforcementValue (mathematics)Law enforcementAccountingCapital marketIndex (typography)EconomicsBusinessLawPolitical scienceComputer scienceFinance

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to provide a conceptual viewpoint of the appropriate theoretical framework for measuring the integrity of capital markets from a regulatory/law‐enforcement perspective. Design/methodology/approach This paper discusses the metrics involved with the abstract concept of measuring the integrity of capital markets. References include measurement tools and measurement principles issued by academic institutions and relevant international organizations. Primary research was conducted by way of the Royal Canadian Mounted Police's Market Integrity Index research project. Findings The paper finds that there is an interplay of forces that determine the relative integrity of a given market over time. Originality/value This paper suggests that critiques of the efficacy of systemic market structure can be evaluated on a quantitative basis.

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)0.016
metaresearch head score (Gemma)0.053
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: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.878

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0240.018
Science and technology studies0.0070.020
Scholarly communication0.0180.013
Open science0.0050.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.069
GPT teacher head0.209
Teacher spread0.139 · 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
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

Citations8
Published2008
Admission routes2
Has abstractyes

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