H.: Acceptance and expectations for cyberclothes by the general public in France
Bibliographic record
Abstract
This study investigated the potential acceptance of cyberclothes by the general public. It provides a global eval-uation, identi¯es centers of interest, expectations, and elements of apprehension for use in everyday life. 206 people (including 157 French) answered questionnaires about seven related topics. Results suggest a positive a priori for such garments. It shows an interest of French people for equipment and services that improve comfort, safety, and communication in particular contexts. It highlights worries about emotion sharing and control of the system. Acknowledgements We would like to thank the respondents to the questionnaire, for their patience and kind-ness. We would also like to express our gratitude to Laurent Cicurel (INSA Lyon, France) and Val¶erie Moreau (Laval Mayenne Technopole, France) for helping disseminate the questionnaire. Understanding the results We based our analysis on the 5-point scale provided to respondents: 1-strongly disagree, 2-disagree, 3-neither agree nor disagree, 4-agree, and 5-strongly agree. We considered that means below 2.5 indicated a signi¯cant trend for rejection while means above 3.5 indicated a signi¯cant trend for acceptance. Cyberclothes: Nature & Prototype Cyberclothes are{in a nutshell{garments possessing special features letting them be used as social markers or tools, and possessing some autonomy [SH05]. Cyberclothes are ¯rst of all clothes, not just computers concealed in fabrics. Their primary concern is to extend the normal functions of clothes, which are either practical or social. The practical
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".