Personal property securities legislation: Analysing the new lexicon
Bibliographic record
Abstract
Grant Gilmore, co-draftsperson of art 9 of the United States Uniform Commercial Code, from which Australia's 'Personal Property Securities Act' 2009 (Cth) is partly derived, likened approaching art 9 to mastering a foreign language. More recently, the Supreme Court of Canada observed, in the context of a discussion of the meaning of 'property' under equivalent legislation: 'For particular purposes Parliament can and does create its own lexicon.' Focusing primarily on the 'Dictionary' contained in the 'Personal Property Securities Act' 2009 (Cth), this article analyses some of the new definitions and vocabulary. It also examines terms whose meanings are only partly defined or simply assumed, terms which appear to lack a statutory definition, and terms whose previously accepted meaning appears to have changed. The underlying theme is that the 'Personal Property Securities Act's' operation cannot properly be understood without a close knowledge of the language in which the legislation is couched. Finally, the article also briefly explores how the language shapes the manner in which the legislative concepts are intellectualised.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".