The use of technology to automate the registration process within the Torrens system and its impact on fraud : an analysis
Bibliographic record
Abstract
Improvements in technology and the Internet have seen a rapid rise in the use of technology in various sectors such as medicine, the courts and banking. The conveyancing sector is also experiencing a similar revolution, with technology touted as able to improve the effectiveness of the land registration process. In some jurisdictions, such as New Zealand and Canada, the paper-based land registration system has been replaced with one in which creation, preparation, and lodgement of land title instruments are managed in a wholly electronic environment. In Australia, proposals for an electronic registration system are under way. The research question addressed by this thesis is what would be the impact on fraud of automating the registration process. This is pertinent because of the adverse impact of fraud on the underlying principles of the Torrens system, particularly security of title. This thesis first charts the importance of security of title, examining how security of title is achieved within the Torrens system and the effects that fraud has on this. Case examples are used to analyse perpetration of fraud under the paper registration system. Analysis of functional electronic registration systems in comparison with the paper-based registration system is then undertaken to reveal what changes might be made to conveyancing practices were an electronic registration system implemented. Whether, and if so, how, these changes might impact upon paper based frauds and whether they might open up new opportunities for fraud in an electronic registration system forms the next step in the analysis. The final step is to use these findings to propose measures that might be used to minimise fraud opportunities in an electronic registration system, so that as far as possible the Torrens system might be kept free from fraud, and the philosophical objectives of the system, as initially envisaged by Sir Robert Torrens, might be met.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".