From Being There to Watching
Bibliographic record
Abstract
Telepresence attendance at academic conferences is now a reality and allows people who cannot attend in person with the opportunity to still be “present.” This is valuable for people who face accessibility challenges, cost or travel restrictions, or limited time for travel. We have deployed and studied the use of telepresence robots at three ACM conferences, Ubicomp/ISWC 2014, CSCW 2016, and CHI 2016, ranging from remote users having dedicated telepresence robots to users sharing telepresence robots both synchronously and asynchronously. In this article, we report on the telepresence offerings along with the user behaviors, experiences, and the social norms found for remote conference attendance. Our results across the studies focus around three main themes: shared vs. dedicated robot usage, identity presentation and the value and challenges associated with it; and local in-person support through proxies and instant messaging backchannels. These themes point to three different areas of design exploration for telepresence robots, pointing out the limitations of existing design solutions with respect to each theme, areas for future telepresence design work, and the value in considering varied telepresence robot solutions, including both dedicated and shared telepresence robots.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
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".