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Record W1916775532 · doi:10.1108/jfc-02-2013-0004

The demographic profile of victims of investment fraud

2014· article· en· W1916775532 on OpenAlex
Mark Lokanan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Financial Crime · 2014
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMandateInvestment (military)TribunalOriginalityBusinessUnit investment trustSecurities fraudValue (mathematics)AccountingFinanceActuarial scienceOpen-ended investment companyEconomicsReturn on investmentLawPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Purpose – The purpose of this study is to examine the demographic characteristics of investors who have been victims of investment fraud in Canada from 1984 to 2008. Design/methodology/approach – Data for this study come from the Investment Dealers Association's tribunal cases that were decided between 1984 and June of 2008. The cases were retrieved from the Securities Regulation Tribunal Decisions database in Quicklaw . Data were collected to examine the demographic profiles of the investors. Findings – The findings indicate that the victims were not particularly rich and a significant proportion borrowed money and opened margin accounts to invest. Those most vulnerable were investors who were retired and had limited investment knowledge. Many also dipped into their savings to fund their future retirement needs. Practical implications – The study is useful for regulators in the securities industry because it paints a demographic portrait of the investors who are more vulnerable to investment fraud. Thus, as part of their investors' education mandate, regulators can tailor their fraud prevention programs to the needs of specific subsets of investors. Originality/value – This is the first study of its kind in Canada that provides a detailed demographic profile of victims of investment fraud. For the first time, data are available to show the occupational classifications, types of accounts and investment objectives of investors who were victims of investment fraud.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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