Just-In-Time Click-Through Agreements: Interface Widgets for Confirming Informed, Unambiguous Consent
Bibliographic record
Abstract
The most common method for supporting consent in computer applications is a "user agreement." When you have installed new software on your computer, or signed up for an Internet service, you have undoubtedly seen an interface screen that presents a User Agreement or Terms of Service. In order to continue, you have had to click on an "I Agree" button or an equivalent label. These interface screens are commonly called "click-through agreements" because the users must click through the screen to get to the software or service being offered [2]. (An alternative label is "click-wrap agreement,” in parallel to more traditional "shrink-wrap" agreements attached to software packaging.) These agreement screens are an attempt to provide the electronic equivalent of a signed user agreement or service contract [3]. By clicking on the "Agree" button, the user is confirming their understanding of the agreement and indicating consent to any terms or conditions specified in the accompanying text.
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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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.021 |
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".