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Taking Care to Play

2015· book-chapter· en· W2501092727 on OpenAlexaff
Shika Card, Huali Wang

Bibliographic record

VenueAdvances in psychology, mental health, and behavioral studies (APMHBS) book series · 2015
Typebook-chapter
Languageen
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsMcGill University
Fundersnot available
KeywordsTransformative learningEthnographyPerspective (graphical)PerceptionContext (archaeology)PsychologySociologyDevelopmental psychologyHistoryVisual artsArt

Abstract

fetched live from OpenAlex

This chapter takes an anthropological perspective to examine how different modes of communication play a crucial role in caregiving for a person with Alzheimer's disease (AD) in the context of modern-day China and to rethink the predominant perception of AD as a condition of degeneration, loss, and disability. This chapter is based on 10 weeks of ethnographic fieldwork research in Beijing, including 13 interviews with clinicians and family caregivers, as well as observational data gathered in diverse therapeutic settings. Putting into question the common impulse to listen primarily for the semantic qualities of speech, the authors argue that alternative and experimental forms of communication make possible a different interactive space between people with AD and their caregivers. This research proposes the notion of play as a particular mode of communication that can enact new and transformative practices of care and, as a result, reconfigure and reaffirm relationships of care.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0070.010
Scholarly communication0.0070.006
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0240.008

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 head0.147
GPT teacher head0.536
Teacher spread0.389 · 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 designObservational
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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