A New Gig for Unconscionability — Equity and Human Dignity in Uber Technologies v. Heller [2020]
Bibliographic record
Abstract
In Uber Technologies Inc. v. Heller, the Canadian Supreme Court affirmed the capacity of the doctrine of unconscionability to protect people working in the ‘gig economy’ from oppressive implications of non-negotiable standard form contracts tendered by drastically more powerful business entities. On the basis of unconscionability, the Court rejected Uber’s attempt to enforce a clause in their non-negotiable standard form contract that would preclude its drivers from invoking employment law rights in a domestic court, having stipulated dispute resolution through arbitration in a foreign jurisdiction at upfront and unaffordable expense to the driver. This case note critically elucidateshow the Court’s decision advances standards of human dignity for working people through an equitable reading of the relevant statute, and subsequently applying the characteristic elasticity of the Equitable doctrine of unconscionability in addressing changing social and economic circumstances and drastic power imbalances between parties.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".