Bibliographic record
Abstract
This article seeks to address a specific aspect of Bills of Rights that tends to be neglected in the literature. That is, the process of how Bills of Rights are drafted. In particular it focuses on the drafting of a particular right-equality with a view to identifying if there is a link between: (a) the manner of how an equality provision is drafted and securing legitimacy of the final product; (b) whether a participative process can influence the formulation and articulation of an equality provision; and finally (c) if the ‘people’ have spoken through this document, does this encourage the judges to take a less restrictive approach in interpreting the equality provision? This task is undertaken by drawing upon the Canadian experience, which then will be used to draw out lessons for those jurisdictions where the process of drafting an equality provision in a Bill of Rights is under way. The article is supported in its conclusions by a series of semi-structured interviews with key players involved in the drafting and interpretation of the equality provision in the Canadian Charter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.090 | 0.049 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 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".