MétaCan
Menu
← Back to cohort
Record W2394748648

Forgery Losses: Banks Beware!

2016· article· en· W2394748648 on OpenAlexaff
Benjamin Geva

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsYork University
Fundersnot available
KeywordsBusiness
DOInot available

Abstract

fetched live from OpenAlex

A widespread perception is that a bank is a custodian of depositors’ money and is under an obligation to guard it for them. From the legal perspective, this is, of course, untrue. It was settled way back in the 19th century that a bank is a debtor of and borrower from its depositor. In that capacity, the bank is authorized to use money on deposit for its own benefit and profit. At the same time, like a borrower liable on a simple monetary debt, a bank is responsible for the return to the depositor/lender of the entire amount borrowed. A bank is not released from liability for the loss of the money on deposit in circumstances that under the law of bailment would have released a custodian—for example, for lack of fault.

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)0.005
metaresearch head score (Gemma)0.043
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 score0.031
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.043
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0150.018
Open science0.0010.005
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0310.010

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 head0.013
GPT teacher head0.215
Teacher spread0.202 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueSSRN Electronic Journal→Same topicBanking stability, regulation, efficiency→French-language works237,207→