Bibliographic record
Abstract
This article is a revised version of a paper delivered at the 33rd Annual Workshop on Commercial and Consumer law, held at the Faculty of Law of the University of Toronto. It is a commentary on Stephen Waddams, Dimensions of Private Law: Categories and Concepts in Anglo-American Legal Reasoning (Cambridge, Press 2003). The article first reviews Waddams' thesis of the inadequacy of simple explanations or categorizations of private law and Waddams' admonition to avoid labeling cases such as contract or tort, as if one involves solely enforcing agreements and the other only wrongdoing. The article then goes on to analyze questions inspired by Waddams' book: What accounts for the popularity of conceptualizing private law? What are the ramifications of the reality that private law is complex and multidimensional? What new approaches to the study of decision-making shed light on the judicial process when judges confront multidimensional problems? The article concludes that analysts should not be sanguine about the ability of judges to handle complexity and that judges make systematic errors in that environment just like everyone else. If categorizing or mapping moves only a few prominent concepts to the forefront, perhaps it performs an important service.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.057 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".