The Drafters’ Dance: The Complexity of Drafting Legislation and the Limitations of ‘Plain Language’ and ‘Good Law’ Initiatives
Bibliographic record
Abstract
Abstract Can we provide legislative drafters with tools to simplify and clarify legislation, and make it more accessible? In the United Kingdom, United States, Canada, Australia, and European Union, through strategies such as the ‘Plain Language’ and ‘Good Law’ (PL/GL) initiatives, it is claimed that the answer is ‘yes’. Though many of the normative intentions underlying such initiatives are commendable, we argue that the pursuit of legislative and legal simplicity, clarity, and accessibility ignores the distinctly ‘complicated’ and ‘complex’ role of legislation and legislative drafters. This leads to a range of contradictory and paradoxical outcomes that undermine these goals. Following a review of the role of legislative drafters and PL/GL initiatives, we use a complexity tool, the Stacey Diagram, to demonstrate and visualize the inherent tensions in the PL/GL position. We show how legislative drafters negotiate their complex environment in a much more subtle, human way than is commonly recognized in PL/GL discourse.
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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 | 0.083 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.070 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".