I Click, You Click, We all Click - But Do We Have a Contract? A Case Comment on Aspenceri.com v. Paysystems
Bibliographic record
Abstract
It is trite to say that e-commerce has exploded over the last several years. Canadian individuals and businesses are entering into thousands and thousands of contracts online all the time. Yet, oddly enough, there is surprisingly little legal certainty or consistency regarding an essential legal question: what approach to online contract formation will create a binding legal contract? Such legal uncertainty is unfortunate, since buyers need to know when to ‘‘beware’’, merchants need to be able to manage risk, and courts need to have clear guidelines in order to be able to render informed, coherent decisions.\nThe issue of online contract formation was recently treated in the Quebec court decision Aspencerl.com v. Paysystems Corporation (Paysystems). The legal argument in the decision differs significantly from existing Canadian and Quebec jurisprudence on the subject of online contract formation. Accordingly, this case com- ment is intended to analyse and critique the Paysystems decision, to discuss and evaluate current approaches to online contract formation more generally, and to provide advice regarding how to mitigate the risk of a finding of non-enforceability.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.028 | 0.020 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".