Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".