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International Regulatory Regimes

2012· book-chapter· en· W2491069864 on OpenAlexaff
Tony Porter

Bibliographic record

VenueInternational Finance · 2012
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Regulation and Crises
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFinancial crisisBusinessPolitical scienceFinancial systemEconomic systemInternational tradeEconomicsMacroeconomics

Abstract

fetched live from OpenAlex

Abstract As finance has become more globalized and global financial crises have become more frequent and severe, a set of international regulatory regimes has been established. These have generally taken the form of relatively informal networks of regulators focusing on particular aspects of the financial system. Over time these regulatory regimes have become more complex. States, operating first through the G7 and then the G20, began to oversee and direct these regimes more actively and politically. The regimes' engagement with internationally active business actors has increased, leading some to see them as having been captured by the industry. Initially they tended to be exclusive, but over time, and especially after the crisis of 2008, the number of countries involved has expanded. The degree and sources of their effectiveness continue to be debated.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.027
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.004
Scholarly communication0.0060.004
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0270.006

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 head0.031
GPT teacher head0.228
Teacher spread0.197 · 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
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

Citations1
Published2012
Admission routes1
Has abstractyes

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