Bibliographic record
Abstract
The rapid development of large interactive wall displays has been accompanied by research on methods that allow people to interact with the display at a distance. The basic method for target acquisition is by ray casting a cursor from one's pointing finger or hand position; the problem is that selection is slow and error-prone with small targets. A better method is the bubble cursor that resizes the cursor's activation area to effectively enlarge the target size. The catch is that this technique's effectiveness depends on the proximity of surrounding targets: while beneficial in sparse spaces, it is less so when targets are densely packed together. Our method is the speech-filtered bubble ray that uses speech to transform a dense target space into a sparse one. Our strategy builds on what people already do: people pointing to distant objects in a physical workspace typically disambiguate their choice through speech. For example, a person could point to a stack of books and say "the green one". Gesture indicates the approximate location for the search, and speech 'filters' unrelated books from the search. Our technique works the same way; a person specifies a property of the desired object, and only the location of objects matching that property trigger the bubble size. In a controlled evaluation, people were faster and preferred using the speech-filtered bubble ray over the standard bubble ray and ray casting approach.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".