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Record W7100429188

The development of the affective value in the entertaining interaction Stéphanie Cardoso; Candidate in Ph.D. Arts, History, Theories and Practices, speciality Design

2015· article· en· W7100429188 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicArt, Technology, and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingObject (grammar)Meaning (existential)Value (mathematics)RobotInterpersonal relationshipUSableFunction (biology)Conceptualization
DOInot available

Abstract

fetched live from OpenAlex

The companionship robots are they susceptible to bring an affective look? The term robot comes from the Czech robota mean slave/work, thus to introduce the concept of companionship at the robot, would thus return to the friendly design of the robot at Rossum ' S father in the play, the R.U.R. (Capek, 1920). It is then a question of meaning the sensitive relations between the man and the robot of companionship. This study aims questioning general public facing robot Aibo dog, and at raising its first feelings-let us recall for this matter that the 1st "friendly robot " was the hero of the animated series, Testuwan Atum, or Astro Boy, (Osamu Tezuka, 1952)-. In this way the designer of Aibo’s software questioned themselves rightly “will like we AIBO as we like Mickey? (Kaplan, 2001) ” This questioning brings to check if the feeling of attachment can be corollary with the function of entertainment, directly resulting from an amusing presence. The objective of the experimentation carried out in May 2006 to the Sciences ’ Center of Montreal then consists in releasing from the formal and relational properties of an affective nature usable again in the design of a new object species, the sensory assistants filling our emotional lacks.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.086
GPT teacher head0.292
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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