Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Identifying links between human personality and attributed robot personality is a relatively new area of human–robot interaction. In this paper we report on an exploratory study that investigates human and robot personality traits as part of a human–robot interaction trial. The trials took place in a simulated living-room scenario involving 28 participants and a human-sized robot of mechanical appearance. Participants interacted with the robot in two task scenarios relevant to a ‘robot in the home’ context. It was found that participants’ evaluations of their own personality traits are related to their evaluations of the robot’s personality traits. The statistical analysis of questionnaire data yields several statistically significant results: (a) Participants do not tend to assign their personality traits to match the robots’, (b) For individual personality traits, participants rated themselves as having stronger personality characteristics compared to the robot, (c) Specific significant correlations were found between participants’ and robot personality traits, and (d) Significant group differences for participant gender, age and technological background are highlighted. The results are discussed in light of developing personalized robot companions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it