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Record W2042466461 · doi:10.1075/is.6.3.03car

Constructing perspectives in the social making of minds

2005· article· en· W2042466461 on OpenAlexaff
Jeremy I. M. Carpendale, Charlie Lewis, Ulrich Müller, Timothy P. Racine

Bibliographic record

VenueInteraction Studies Social Behaviour and Communication in Biological and Artificial Systems · 2005
Typearticle
Languageen
FieldPsychology
TopicSocial Representations and Identity
Canadian institutionsUniversity of ManitobaUniversity of VictoriaSimon Fraser University
Fundersnot available
KeywordsPerspective (graphical)Joint attentionRationalityPsychologyIndividualismEpistemologyPerspective-takingSocial psychologySociologyCognitive psychologyCognitive scienceDevelopmental psychologyAutismComputer scienceEmpathy

Abstract

fetched live from OpenAlex

The ability to take others’ perspectives on the self has important psychological implications. Yet the logically and developmentally prior question is how children develop the capacity to take others’ perspectives. We discuss the development of joint attention in infancy as a rudimentary form of perspective taking and critique examples of biological and individualistic approaches to the development of joint attention. As an alternative, we present an activity-based relational perspective according to which infants develop the capacity to coordinate attention with others by differentiating the perspectives of self and other from shared activity. Joint attention is then closely related to language development, which makes further social development possible. We argue that the ability to take the perspective of others on the self gives rise to the possibility of language, rationality and culture.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0030.039
Scholarly communication0.0090.009
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.277
GPT teacher head0.492
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2005
Admission routes1
Has abstractyes

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