A Linguistic Insight into the Legislative Drafting of English-Speaking Jurisdictions
Bibliographic record
Abstract
A Linguistic Insight into the Legislative Drafting of English-Speaking Jurisdictions: The Use of ‘Singular They’ Gender specificity in legislation started being questioned in the late 20th century, and the need to reform the way in which laws have been written for more than one-hundred years has been particularly evident in English-language jurisdictions. In the 1990s and 2000s, the adoption of a plain English style forced legislative drafters to avoid sentences of undue length, superfluous definitions, repeated words and gender specificity with the aim of achieving clarity and minimizing ambiguity. Experts in the legal field have suggested reorganizing sentences, avoiding male pronouns, repeating the noun in place of the pronoun, replacing a nominalization with a verb form, resorting to ‘the singular they’. This article gives a linguistic insight into the use of ‘singular they’ in English, beginning with a historical background and going on to assess the impact of its use in the primary legislation issued in a selection of English-language jurisdictions (Australia, Canada, New Zealand, the UK, the US) in the last decade (2008-2018). Given the environment of legislative drafting techniques, where considerable reliance on precedent is inevitable, proposals to change legislative language may produce interesting results in different jurisdictions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".