CONTRASTING STACK-BASED AND RECENCY-BASED BACK BUTTONS ON WEB BROWSERS
Bibliographic record
Abstract
People frequently use the ubiquitous Back button found in most Web browsers to return to recently visited pages. Because all commercial browsers implement Back as a stack, previously visited branches of the tree are pruned; this means that people can quickly navigate back up the tree. The problem is that previously seen pages on alternate child branches are no longer reachable through Back. An alternate method is to implement Back on a recency model, where all visited pages are placed on a recency-ordered list with duplicates removed. This means that all previously seen pages are now available via Back. Because advantages and trade-offs exist in both methods, we performed a study that contrasted how people used stack vs recency-based Back. We found that people have a naïve mental model of how the conventional stack-based Back works, typically perceiving it as a recency list. People are also poor predictors of what pages will be displayed with both types of Back buttons. Finally, people seem evenly split over their preference of a stack vs recency-based Back button.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".