Using Network Citation Analysis to Reveal Precedential Archetypes at the Supreme Court of Canada
Bibliographic record
Abstract
The chapter uses network analysis and natural language processing to identify precedential archetypes based on 9,295 cross-citations between Supreme Court of Canada constitutional cases since 1983. Citation analyses of apex courts have a long history. However, researchers can gain more sophisticated insights from these citations by combining network analysis with natural language processing tools. This chapter first discusses existing citation analyses of the Supreme Court of Canada centred around simple citation counts and contrasts them with a legal data analytics approach. It then uses the latter, new perspective to create four precedential archetypes that represent different trajectories or life cycles of Supreme Court precedents: (1) the “eternal star” that is cited consistently and widely; (2) the “central focal point” whose treatment of a cross-cutting issue projects it to the very centre of the case law network; (3) the “niche anchor” that exhibits consistent relevance in a narrower or more peripheral area of law; and (4) the “displaced pioneer” that is overtaken, but not overruled, by subsequent jurisprudence. The chapter demonstrates that network analysis combined with natural language processing can quantitatively trace the rise and fall of precedent in ways that are normatively more aligned with how legal scholars think about precedent.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.031 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".