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Record W4317366227 · doi:10.3138/9781487547943-014

4.4 The Example of Ontario

2022· book-chapter· en· W4317366227 on OpenAlexaboutno aff
Michael J. Trebilcock

Bibliographic record

VenueUniversity of Toronto Press eBooks · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsHistoryComputer science

Abstract

fetched live from OpenAlex

84 Paradoxes of Professional Regulation 39 Supporters of the new rule argue that the open-ended meaning of the term will provide for greater regulatory flexibility, although critics suggest that the new terminology used for broker-dealers as opposed to investment advisers (subject to a fiduciary best interests rule) fails to provide a comprehensive and consistent approach.40 A 2015 report by the White House Council of Economic Advisers found that biased advice drained $17 billion a year from retirement accounts.See Council of Economic Advisors Report, "The Effects of Conflicted Investment Advice on Retirement Savings" (2015), https://obamawhitehouse.archives.gov/sites/default/files/docs/cea_coi _report_final.pdf.41 The rules were vacated on the basis that DOL exceeded its statutory authority, although the DOL is working with the SEC to resurrect the fiduciary rule.42 See Constitution Act, 1982, being Schedule B to the Canada Act 1982 (UK), 1982, c 11. 43 In recent decades, as financial markets have become more complex and international in scope, efforts to form national regulatory organizations to administer and enforce laws and regulations across Canada have developed.There have been a number of attempts to bring securities regulation under federal constitutional jurisdiction.In 2011, the Supreme Court of Canada held that the federal government did not have constitutional jurisdiction to enact a securities act that it had proposed, and the SCC

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: yes
Not applicablelow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: yes
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.000
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.055
Threshold uncertainty score0.399

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0130.005
Scholarly communication0.0050.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0320.003

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 head0.035
GPT teacher head0.223
Teacher spread0.188 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Theoretical 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
Published2022
Admission routes1
Has abstractno

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