Bibliographic record
Abstract
Chapter Eleven offers a comparative excurses and looks in much more detail at Canadian developments and, in particular, the impact of the decision of the Supreme Court in Bhasin referred to in the preceding paragraph. The chapter looks at how Canadian law has developed since that landmark decision. This allows insight into how good faith may impact when it becomes an overarching norm. The position in both England and Scotland is, of course, currently far removed from this. The response within Canada to Bhasin has been for the most part cautious and most decisions would have been decided in the same way even if the Supreme Court had not spoken. Of late there have been indications of greater movement and in Heller the Supreme Court has taken the radical step of allowing the substantive fairness of the bargain to be challenged. Bhasin also offers a route to prevent contracting-out but the issue does not yet appear to have been explored in detail in the employment context. The conclusion is reached that Canadian jurisprudence may well be of most relevance where it addresses specific issues (such as regulation of discretion) that have troubled the courts in the UK. The outcome in many of the Canadian cases was not contingent upon the existence of an overarching norm but merely on good faith being relevant to the context in question.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".