Can Quantitative Methods Complement Doctrinal Legal Studies? Using Citation Network and Corpus Linguistic Analysis to Understand International Courts
Bibliographic record
Abstract
Abstract A recent editorial in this journal stressed the need to rearticulate the methodology – and thereby the distinctiveness – of international law in the context of blurring disciplinary lines between international law and international relations. The aim of this article is to contribute to the methodological aspect of the debate. First, the article outlines a legal empirical approach, which complements legal methodology of international law with empirical tools and techniques such as citation network analysis and corpus linguistics. Second, the article applies the approach on the case law of two European courts: the Court of Justice of the European Union (CJEU), and the European Court of Human Rights (ECtHR). It demonstrates how the study of case citations and the language of courts enhance the validity, reliability, and transparency of the established legal method. In particular, scholars of international law gain a stable and complete quantitative basis for a further in-depth study of case law, precedent and interpretation. Additional benefit stems from a set of transparent criteria by which to criticize the jurisprudence of international courts. Firmer ground emerges from which to evaluate the courts’ role in the political process, their societal impact and their legitimacy. At the same time the approach preserves the main features of the distinct legal methodology of international law – especially its attention to legal detail.
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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 | 0.034 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.030 | 0.029 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".