The Plain Meaning Rule: A Quibble about Nomenclature and a Lot More
Bibliographic record
Abstract
Abstract In the Cayman Islands, a British Overseas Territory, the proper understanding of the plain meaning rule (or literal rule) of statutory interpretation remains unclear. In its most basic iteration, the rule states that, where a statute is clear and unambiguous, the words must be given their natural and ordinary signification; there is no room for interpretation. That notwithstanding, to this day, even the meaning of the rule, as opposed to its application, still sometimes sparks debate in the Commonwealth. In 2015, a judge of the Grand Court in Cayman held that once a provision in a statute is clear and unambiguous, a court does not need to read the provision in its broader context. In a subsequent case, in 2018, a court of coordinate jurisdiction disagreed. It ruled that, even where a provision appears to be clear and unambiguous, a court must still read the statute in its fuller context in order to decipher the legal meaning in that particular context. This article discusses the plain meaning rule with a view to elucidating its proper understanding while questioning the appropriateness of its continuing nomenclature especially in light of developments in recent decades.
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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 | 0.043 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.052 |
| Scholarly communication | 0.014 | 0.031 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 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".