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Record W7100043514

The Background By

2011· article· en· W7100043514 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsnot available
Fundersnot available
KeywordsShadow banking systemShadow (psychology)Leverage (statistics)Financial crisisContext (archaeology)Principal (computer security)Bank runBanking industryFinancial services
DOInot available

Abstract

fetched live from OpenAlex

The growth of shadow banking in recent decades has changed the concept of banking. It has meant less deposit-taking and lending and more market-oriented banking activities, including in particular a growing trade in securitized products. However, shadow banking is opaque; a problem that was underlined in the recent financial crisis. Does the experience of the financial crisis and its links to the riskiness of banking mean bank re-regulation is necessary? In the Canadian context at least, better reporting of bank risk seems to be a more appropriate way than re-regulation to prevent financial turmoil from arising in this area. Market-oriented operations should be more exposed to daylight, to enable a better evaluation of true bank risk, and regulatory agencies should require detailed reports on activities generating noninterest income. Better indicators of leverage need also to be developed, owing to leverage’s role as the principal channel of bank risk-taking. The advent of shadow banking has fundamentally altered the nature of banking. Where once banks were mainly in the traditional business of taking deposits and making loans, they have come to rely on marketoriented and off-balance-sheet activities to generate a major share of their income. The problem: these activities are utterly opaque, as underlined by their role in the sub-prime financial crisis. The question: what to do about it? Is re-regulation the answer or is there a better way to avoid a repeat of the financial turmoil that originated in this shadowy area?

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.630
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.003

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 head0.242
GPT teacher head0.468
Teacher spread0.225 · 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; both teacher heads agree on what is shown here.

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
Published2011
Admission routes1
Has abstractyes

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