Bibliographic record
Abstract
We have grown accustomed to the incredibly effortless “click to agree” mechanism as part of the quotidian in our digital lives. We routinely agree to terms and conditions to visit websites or consent to the “fine print” that we rarely read in online transactions because we assume those terms and conditions, had we the time to read them, are not unexpected. However, when an algorithm becomes an actor in this process, selecting the standard terms to which we are habitually agreeing, it gives us pause for thought: what are the implications of an algorithm filling in for human expertise in the contracting process? This chapter will investigate the challenges and opportunities posed by the emerging algorithmic contract in Canadian contract law. These types of contracts are contingent upon algorithmic decision-making, and supplemental to human decision-making in the contracting process. Through an analysis of the Canadian landmark case of Uber Technologies v. Heller (2020) as an algorithmic contract, this chapter sees the rise of the algorithmic contract as part of the contentious and continuing story of the technologizing of contract. Uber v. Heller may be viewed as a “high-water point” in contract law, thus providing the opportunity for future case law to deal with issues created by algorithmic contracts. The chapter concludes by advancing one avenue for which there is an existing footprint whereby contract law could deal with algorithmic contracts in the business-to-consumer context. This route may be suggestive for other areas grappling with the algorithmic turn in law.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.071 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".