Bibliographic record
Abstract
All users of complex software make decisions that they may later wish to change. Many computer systems have tools to support this need for revision, such as the undo command. However, the common history tools (like undo) do not support exploratory, epistemic interaction well. And there are common, non-specialized tasks that are difficult in common computer systems, but would be much easier with improved support for managing interaction history. Desktop computing environments have well-established norms for how undo works, but there is room to explore this in newer computing environments, such as the Web and surface computing, as their design culture has not stabilized to the same extent. We argue that history tracking needs to be more accessible to users.We developed a prototype JavaScript library for Web applications that lets users keep a history of all their interaction states, including those that would be discarded by using a traditional stack-model undo system. The history is presented to users in a tree structure similar to the model used in source control software. We ran a usability study of our system with two applications designed to encourage the kind of exploratory behaviour we wanted to support. We identified usability improvements that could be made, but the study suggests that this kind of system could be generally useful even in non-specialized fields.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".