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Record W2328245325 · doi:10.1515/eplj-2012-0005

Reforming Personal Property Security Law – Some Implications for the Baselines of Priority Regulation

2012· article· en· W2328245325 on OpenAlexaboutno aff
Henry Matz

Bibliographic record

VenueEuropean Property Law Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and International Contract Law
Canadian institutionsnot available
Fundersnot available
KeywordsPersonal propertySecurity interestUnitary stateCreditorProperty (philosophy)DebtorLegislationProperty lawGermanLaw and economicsOrder (exchange)Argumentation theoryLawLienBusinessPolitical scienceProperty rightsEconomicsFinanceGeography

Abstract

fetched live from OpenAlex

The following text wants to add to the existing discussion on the reform of personal property security. It deals with an important aspect of regulation of secured transactions, i.e., how conflicts between creditors with interests in the same object of the debtor’s property should be solved. At the centre of the discussion are the baseline criteria used to order priority conflicts. In order to explore those basic criteria, the text compares two models of legislation in the field of personal property security: The unitary and functional approach and the so-called “formal” approach, the latter admitting the coexistence of different kinds of institutions of personal property security. Comparing those two approaches, the text focuses on the US-American law (Article 9 UCC) and the Canadian Personal Property Security Acts (PPSA) as prominent forerunners of today’s unitary and functional legislation, on the one hand, and on German and Swiss law of secured credit as prominent examples for continental European law, on the other hand. Using the example of German and Swiss law, the comparison wants to provide for some argumentation on how continental European law of secured credit could be amended in order to achieve effective and efficient use of security.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.315
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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