Toward a Framework to Define the Outer Boundaries of Good Faith in Contractual Performance
Bibliographic record
Abstract
Since Bhasin v. Hyrnew, the application of good faith in contract law has varied and its outer boundaries have been unclear. To understand the variance in judicial applications of good faith, this article offers a framework that both explains judicial tendencies and prescribes a template for judges to justify differing approaches. The proposed framework distils the application of good faith to the interaction between institutional variables (the factors that determine judicial reasoning) and transactional variables (factors that arise from the context in which the contract arises). The article develops a taxonomy of the various alternative ways of approaching the doctrine of good faith resulting from the overlap of two institutional variables, the possible functions that good faith may serve and the criteria that inform the prescriptive content of good faith. The article then demonstrates how transactional variables inform the types of institutional variables a judge employs. Two cases that were recently decided by the Supreme Court of Canada demonstrate that by explicitly adhering to the proposed framework, judges can be more transparent about how and why they employ good faith in differing contexts.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".