Bibliographic record
Abstract
Abstract The enactment of Article 9 of the Uniform Commercial Code in the USA has had a profound influence on the reform of secured transactions law in other countries. The operational principles that animate Article 9 were first transplanted into Canada and later into New Zealand. In the last two decades, at least 25 countries have passed personal property security legislation (PPSA) based on these principles. On one level, one could claim that Article 9 has been transplanted into each of these 25 countries. However, on another level this story is far too simplistic. If one examines the various statutes, it becomes clear that a more complex process has been at work in which there has been innovation as well as borrowing. These innovations, in turn, influence the borrowings of other countries that enact a PPSA. In this highly dynamic environment the source of borrowing can be difficult to identify. This article examines the nature and extent of the borrowings that occur in connection with the reform of secured transactions in countries that have enacted a PPSA. It will identify three major templates that are available—namely, the most recent version of Article 9, the Canadian/New Zealand model, and the UNCITRAL Model Law. These templates will be reviewed in order to find markers that are present only in that template and not in the other two. These markers will be used to ‘fingerprint’ the PPSA legislation in other countries in order to measure the extent to which the jurisdiction has borrowed from each of the three templates. The article will conclude with a number of observations about the path of secured transactions law reform on an international level.
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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".