Compromise and Public Debate in Processes of Constitutional Reform: the Canadian Case
Bibliographic record
Abstract
In this article, I concentrate on one central issue that has arisen since the 1987 Meech Lake Accord and the 1992 Charlottetown Accord failed to secure sufficient popular support to allow their ratification. Many theorists have argued that there exists an unavoidable disjunction between the kind of compromise agreement that can come out of complex intergovernmental negotiations and the type of outcome that a majority of citizens might be made to support. Any agreement produced by formal talks can be presumed to have involved significant logrolling and be made of various, mutually dependent, sets of compromises. Such a composite agreement, it is argued, has but little chance to stand the test of public debate and attract sufficient popular support to ensure ratification. In the present article, I want to revisit the story of the failed Charlottetown Accord to show the ways that the risks of disjunction can be alleviated. More specifically, I attempt to show that referendums, if properly integrated in the process, can have positive effects both on the negotiations themselves and on the ability of the parties concerned to rise to the challenge of public justification.
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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.067 | 0.041 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.018 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".