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Record W4380087520 · doi:10.54691/bcpbm.v46i.5089

Subprime Crisis and Regulatory Responses

2023· article· en· W4380087520 on OpenAlexaff
Mohan Qin

Bibliographic record

VenueBCP Business & Management · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsColumbia College
Fundersnot available
KeywordsSubprime mortgage crisisFinancial crisisFinancial systemGreat DepressionSubprime crisisLeverage (statistics)Real estateEconomicsMonetary policyFinancial marketEconomic bubbleEconomic policyBusinessFinanceMonetary economicsPolitical scienceMacroeconomics

Abstract

fetched live from OpenAlex

The 2008 financial crisis brought about the worst economic depression since World War II. This paper covers the three main components of the economic meltdown, along with its cause, consequences, and countermeasures. The crisis emerged against a backdrop of rapidly expanding credit, high risk-taking, and heightened financial leverage. In particular, factors related to the real estate bubble are discussed, among which the subprime mortgage crisis has received widespread attention. The various impacts caused by the crisis are irreversible and affect the financial market to this day. Therefore, discussing various measures after the crisis is helpful to the development of the financial field. Whether it is analyzing monetary policy or fiscal policy, or the ever-renewing Basel Accords, they all help to improve global imbalances. Although the financial crisis emerges in the American market first, its widespread impact is beyond the scope. In this paper, the whole development of the 2008 financial crisis will be deeply discussed.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.027
GPT teacher head0.234
Teacher spread0.207 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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