Uniform Case Naming Guidelines November 2006 i Uniform Case Naming Guidelines The Origin of Case Naming
Bibliographic record
Abstract
[1] When law professionals are referring to a case in their pleadings, lectures or commentaries, they often refer to a case simply by using the surname of the main party involved in the case. They can do this because in a given context, referring to a surname often suffices to identify a specific case. For example, in the context of Charter litigation, every Canadian lawyer knows what the Oakes case is about. This intuitive and informal mean of identifying a case has been common practice since the very beginning of case law reporting. Modern citations to cases still include such a case name, often called “style of cause ” in Canada1. [2] Not so long ago, the case name was one of the few tools law researchers could use to locate and track cases in printed reports2. In the early 80’s, the lack of consistency of the case name among publishers was considered a major impediment for case identification and retrieval3, and thus for the reliability of legal research4. [3] In 1987, in an effort to improve case naming uniformity, Canadian law publishers gathered together through the Canadian Law Information Council (“CLIC”) and developed the Standards for Case Identification5. These “CLIC Standards ” provided a
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 | 0.157 | 0.317 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.020 | 0.022 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".