Determinate/Indeterminate Duality: The Necessity of a Temporal Dimension in Legal Classification
Bibliographic record
Abstract
The objective of this article is to reconcile the difficulties in legal classification that arise when subject matter is viewed from a purely spatial, i.e., a two or three-dimensional, perspective. At issue is whether the dynamic complexity of legal reasoning can be represented through a process of static classification. The difficulty with traditional approaches to classification is that while legal reasoning makes use of concurrent concepts to resolve issues, classification systems operate with mutually exclusive classes that do not permit representation of reiterative reasoning processes. Using the example of the neologism of "propertization, "an issue of increasing concern in the field of intellectual property, demonstrate that a single classification system can represent both the delerminacy the author seeks to and indeterminacy of legal concepts as they are used to resolve legal problems without sacrificing the clarity presumably required for the rule of law to operate. Resolution requires adopting a classification system that makes use of both a temporal and spatial perspective. By adopting a temporal perspective in addition to a more traditional spatial perspective, we are able to expandour focus from the products of legal classification to legal classification as a process. We can then examine the dynamic relationship of relativity between legal concepts as they operate in context, rather than limiting our analysis to the static relationship of demarcation that exists when legal classes are examined in the abstract.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".