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Record W2126699899

A National Systemic Risk Clearinghouse

2012· article· en· W2126699899 on OpenAlexaffabout
Cristie Ford, Hardeep S. Gill

Bibliographic record

VenueeYLS (Yale Law School) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSystemic riskIssuerGovernment (linguistics)BusinessJurisdictionHedge fundCapital marketFinanceEconomicsLawPolitical scienceFinancial crisis
DOInot available

Abstract

fetched live from OpenAlex

In December 2011, in Reference re Securities Act, the Supreme Court of Canada dashed the Canadian federal government’s hopes of being able to create a federal securities regulator. Instead, it left the constitutional jurisdiction over “day-to-day operations of the securities markets” with the provinces and allocated to the federal government responsibility for just two things: data collection, and the management of systemic risk. Our claim in this essay is that the Reference can be understood as an invitation to create a meaningful and ambitious national systemic risk regulator for the securities markets. The essay points to five recent examples (the use of derivatives by Canadian issuers; LTCM and hedge fund activity; the Asset-Backed Commercial Paper crisis in Canada; the 2010 Flash Crash and high frequency trading; and money market mutual funds) to argue that the day-to-day operations of issuers, registrants, and regulators in the capital markets are constitutive of, and inextricable from, systemic risk. Regulating systemic risk therefore requires some degree of oversight of the underlying activities, plus deep information channels into local markets. In allocating data collection responsibilities to the federal government, the Reference is giving it a potentially significant tool. The Reference opens the possibility that the federal government can create not an overlapping fourteenth securities regulator, but an active “clearinghouse” regulatory body that sets broad goals and regulatory requirements, while leaving detailed implementation of regulation to the provinces.

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)0.027
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.603
Threshold uncertainty score0.789

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.039
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0110.007
Scholarly communication0.0150.007
Open science0.0020.006
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0110.003

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 head0.020
GPT teacher head0.279
Teacher spread0.259 · 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 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

Citations0
Published2012
Admission routes2
Has abstractyes

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