Effective Remediation in Public Procurement: Damages or Judicial Review?
Bibliographic record
Abstract
In Canada, contract liability is generally seen as the primary means for addressing public contract award disputes. However, courts are increasingly recognising the role that judicial review can play in the remediation of procurement disputes. This development is important given that governments across Canada have committed themselves through international trade agreements to give suppliers the right to “effective remedies”. The question that now arises is to what extent Canadian law measures up to this international standard and how to make tendering remediation more effective. This essay explains to what extent contract liability for lost profits can be described as the principle means of regulating the public tendering process and the problems associated with this declining trend. The essay then turns to practical steps that can be taken so as to avoid public liability while ensuring fairness and respecting Canada’s international commitments. The author argues in favour of a more financially effective approach to remediating tendering disputes, taking into account comparative perspectives on how to understand the complementary role between damages and judicial review.
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 | 0.053 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.019 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".