Stating it Simply: A Comparative Study of the Quantitative Readability of Apex Court Decisions from Canada, Australia, South Africa, the UK, and the USA
Bibliographic record
Abstract
Even though common law courts create and articulate the law within their decisions, we know surprisingly little about the quantitative readability levels of any single national apex court’s decisions, and even less about how any one apex court’s readability levels compare to those of other similar apex courts. This article offers new data and analysis that significantly reduces our blind spots in these areas, by reporting the results of an original empirical study of the readability of judicial decisions released in 2020 from the apex courts of five English-speaking jurisdictions. The article draws on applied linguistics theory and Natural Language Processing techniques in order to provide both uni- and multi-dimensional readability scores for the 233 judicial decisions (comprising more than 3 million words of text) that form the corpus of this study. The results show that readability levels vary by approximately 50% between the most- and least-readable jurisdictions (USA and Australia, respectively). The article then analyzes the data comparatively in order to determine whether institution- or jurisdiction-specific factors are capable of explaining readability variances between the different courts. The article concludes that certain comparative factors, such as the average panel size used by each court, and the ratios of both former law professors and women who sit on panels in each jurisdiction, are capable of explaining 23.7% of the total variances in readability scores. These findings may help judicial and executive branch decision-makers to better understand how their court’s decisions stack up against other courts in terms of readability, and offer insights as to how readability levels could be enhanced.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".