How people use orientation on tables
Bibliographic record
Abstract
In order to support co-located collaboration, many researchers are now investigating how to effectively augment tabletops with electronic displays. As far back as 1988, orientation was recognized as a significant human factor issue that must be addressed by electronic tabletop designers. As with traditional tables, when people stand at different positions around a horizontal display they will be viewing the contents from different angles. One common solution to this problem is to have the software reorient objects so that any given individual can view them 'right way up.' Yet is this the best approach? If not, how do people actually use orientation on tables? To answer these questions, we conducted an observational study of collaborative activity on a traditional table. Our results show that the strategy of reorienting objects to a person's view is overly simplistic: while important, it is an incomplete view of how people exploit their ability to reorient objects. Orientation proves critical in how individuals comprehend information, how collaborators coordinate their actions, and how they mediate communication. The coordinating role of orientation is evident in how people establish personal and group spaces, and how they signal ownership of objects. In terms of communication, orientation is useful in initiating communicative exchanges and in continuing to speak to individuals about particular objects and work patterns as collaboration progresses. The three roles of orientation have significant implications for the design of tabletop software and the assessment of existing tabletop systems.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".