Bibliographic record
Abstract
Abstract This chapter defines bills of rights as instruments giving an overarching legal status to a broad set of fundamental human or civil rights. It then argues that, despite this common shape, bills of rights differ significantly in terms of their designed strength and nature. This divergence can be usefully captured by the variable of bill of rights institutionalization (BORI). This new quantifiable measure is based on the instrument's legal status, its rigidity, and the scope of its rights protection. BORI scores are calculated for thirty‐six democracies and correlated against a number of possible determinants of bill of rights outcomes. Not adopting a bill of rights during political transition (e.g. independence) and having a British heritage are both strongly correlated with low BORI scores. All the Westminster democracies – Australia, Canada, New Zealand, and the United Kingdom – share both these characteristics. The chapter closes by providing a précis three factors which make close qualitative study of these four cases particularly interesting: (a) very low average BORI scores, (b) a common trend towards adoption of a bill of rights, and (c) substantial divergence in BORI between the cases.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".