Bibliographic record
Abstract
In social Human-Robot Interaction (sHRI) people have studied social interactions with awkward, confrontational, or unsettling robots. In order to create these situations, researchers often secretly control the robot (the "Wizard of Oz", WoZ, technique), use confederates (researchers pretending to be participants), or the researchers themselves create the desired social condition. While these studies may be antagonistic, they are designed to be ethical; when conducting a study, IRB (Institutional Review Board) processes are in place to assess the study design for potential risk to participants, and to ultimately protect the public. However, these processes do not generally involve assessment of impact on the researchers conducting the study. In our own work, we have noted how researcher "wizards" in social HRI experiments, particularly those which place participants in awkward or confrontational situations, can themselves be negatively impacted from the experience when their experiment protocol has them antagonize, deceive, or argue with participants. In this paper, we explore how experimental design can impact the wellbeing of the researchers, particularly for wizards in social HRI experiments. By building a psychological grounding for the impact on people who do socially stressful actions, we evaluate the potential for researcher social stress in recent sHRI studies. Our summary and discussion of this survey results in recommendations for future HRI research to reduce the burden on wizards in their own experiments.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".