Bibliographic record
Abstract
In Dimensions of Private Law, Professor Stephen Waddams describes the obstacles that an adequate classification of private law must overcome. The purpose of this essay is to offer a theoretical account of legal classification that explains how these obstacles can be overcome and what the resulting classification of private law might look like. I begin with the catalogue of obstacles that Waddams presents and argue that, because they are rooted in misconceptions about the classificatory project, they pose no threat to an adequate conception of legal classification. In search of such a conception, I consider how three great legal theorists – Aristotle, Kant, and Hegel – answer three fundamental classificatory questions about private law. First, what is the unitythat underlies the seemingly chaotic array of legal instances? Second, what is the principle of differentiationthat applies to this unity? Third, how are legalinstancessubsumed under this differentiated unity? The focus of this essay is the enduring significance of Kant’s conception of legal classification, which provides an alternative to Waddams’ conception and offers a set of coherent answers to the fundamental classificatory questions. In contrast, both Aristotle and Hegel respond to the fundamental classificatory questions by providing a conception of the unity of private law that fails to cohere with their ensuing accounts of its differentiation.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.053 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| 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".