Bibliographic record
Abstract
Despite the long development of Graphical User Interfaces, working with multiple graphical objects remains a challenge, due to the difficulties of forming complex selections, ambiguities of operations, and tediousness of repetitively unselect-reselect or ungroup-regroup objects. Instead of tackling them as individual problems, we attribute it to the lack of system support to the general selection-action cycles. We propose Collection Objects to not only support a single fast selection-action cycle but also allow multiple cycles to be chained together into a fluid workflow. Collection Objects unifies selection, grouping, and manipulation of aggregate selections into a single object, with which selection can be composed with various techniques, modified for later actions, grouped with objects inside still directly accessible, and quasi-moded for less context switching. We implemented Collection Object in the context of a vector drawing application with simultaneous pen and touch input. Results of an expert evaluation show that Collection Objects holds considerable promises for fluid interaction with multiple objects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".