Click-Through Agreements: Strategies for Avoiding Disputes on Validity of Assent
Bibliographic record
Abstract
Although case law surrounding click-through agreements is still very sparse, it has evolved sufficiently to discern trends and policies in the small number of cases decided so far. These trends and policies can assist transactional lawyers in advising clients on setting up and using electronic form contracts. They also can assist litigators in continuing to argue and settle disputes on click-through agreements. The Working Group on Electronic Contracting Practices, within the Electronic Commerce Subcommittee of the Cyberspace Law Committee of the Business Law Section of the American Bar Association, assembled a set of fifteen Strategies for avoiding disputes on the validity of the mutual assent process, as well as a bibliography of existing United States and Canadian case law and commentary on click-through agreements. It presented the Strategies and the accompanying bibliography at the ABA Annual Meeting in Chicago on August 5, 2001. That document, slightly modified based on feedback from that presentation, appears at the end of this Article.This information or any portion thereof may not be copied or disseminated in any form or by any means or downloaded or stored in an electronic database or retrieval system without the express written consent of the American Bar Association.
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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 | 0.035 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".