Bibliographic record
Abstract
The object of this type of fraud is not to steal land. Rather, it is to steal money from lenders. There are two types of mortgage fraud. Value fraud schemes, which will not be discussed further in this article, artificially inflate property values to dupe mortgage lenders. In identity fraud, of which Ms. [Zivic] was a victim, the fraudster impersonates the true property owner. The fraudster forges the owner's signature in sale documentation filed at land registry offices to deceitfully obtain ownership on paper. He or she then sells the land to another rogue or uses that property to obtain money through lines of credit or mortgages from unsuspecting lenders. The imposters abscond with the money and the mortgages immediately slide into default. The innocent owner, who knew nothing of what was happening, and lenders, who relied upon the officially registered documentation, look to each other for compensation. While landowners essentially have no opportunity to discover that their property is being fraudulently sold and mortgaged, much less to prevent the fraud, several recent judicial decisions have applied the concept of deferred indefeasibility. In Household Realty Corp. v. Liu (2005), Ms. Chan forged her husband's signature to three new mortgages when he was out of the country on business for extended periods of time. This enabled her to obtain additional mortgages on a property she jointly owned with her husband. She forged his signature on a Power of Attorney and then used it to register a mortgage from a bank. The Ontario Court of Appeal held that the bank and a subsequent lender, neither of which participated in the fraud, were bona fide mortgagees for value and their mortgages were valid. The property was therefore subject to the mortgages even though Ms. Chan's husband had not signed them. In other cases, the courts have found in favour of the innocent landowners. In Rabi v. Rosu (2006), unknown individuals posed as Rabi to transfer title of his condo to Rosu. A new $247,860 mortgage was obtained through the TD Bank. The mortgage broker pocketed $30,000 for assisting the transaction. The individuals who orchestrated the fraud disappeared with the money. Rabi only became aware of the fraud after receiving a City of Toronto tax bill in Rosu's name and, a short time later, a request from the mortgage company. Rabi successfully had the title of the condo restored to his name and the fraudulent mortgage set aside. The Ontario Supreme Court said a more prudent exercise of due diligence on the part of the bank, such as an in-person appraisal, may have prevented the fraud. The Court even ordered that the Bank pay Rabi legal costs of $2,500.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.610 | 0.386 |
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".