Proxemics in Human-Computer Interaction (Dagstuhl Seminar 13452)
Bibliographic record
Abstract
In 1966, anthropologist Edward Hall coined the term "proxemics." Proxemics is an area of study that identifies the culturally dependent ways in which people use interpersonal distance to understand and mediate their interactions with others. Recent research has demonstrated the use of proxemics in human-computer interaction (HCI) for supporting users' explicit and implicit interactions in a range of uses, including remote office collaboration, home entertainment, and games. One promise of proxemics is the realization of context-aware environments, which have been extensively pursued since Marc Weiser's seminal paper, "The computer for the 21st century," written in 1991. However, the potential of proxemics in HCI is still underexplored and many research questions remain unanswered. With the growing interest in using proxemics, we organized the Dagstuhl Seminar 13452 on the topic. "Proxemics in Human-Computer Interaction," was held from November 3-8, 2013, and it brought together established experts and young researchers from fields particularly relevant to Proxemic Interactions, including computer science, social science, cognitive science, and design. Through an open keynote, mini talks, brainstorming, and discussion in breakout sessions, seminar attendees identified and discussed challenges and developed directions for future research of proxemics in HCI.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.022 |
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".