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Record W2341773072 · doi:10.3390/soc6020014

Considering Material Culture in Assessing Assistive Devices: “Breaking up the Rhythm”

2016· article· en· W2341773072 on OpenAlexafffund
Sharon Anderson, Kerri Kaiser Gladwin, Nancy E. Mayo

Bibliographic record

VenueSocieties · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsMcGill UniversityUniversity of Alberta
FundersCanadian Stroke Network
KeywordsMeaning (existential)NarrativeObject (grammar)Assistive technologyPsychologyAssistive deviceAestheticsComputer scienceHuman–computer interactionMedicineLinguisticsPsychotherapistPhysical medicine and rehabilitationArtificial intelligenceArt

Abstract

fetched live from OpenAlex

This paper reports on a project that looked at the meaning stroke survivors assigned to assistive devices. Material culture theory served as a framework to help stroke survivors explicitly consider [dis]ability as a discursive object with a socially constructed meaning that influenced how they thought about themselves with impairment. Material culture theory informed the design (taking and talking to their peers about photos of anything that assisted) and analysis of the meaning of the assistive devices project. In our analysis of the narratives, survivors assigned three types of meanings to the assistive devices: markers of progress, symbolic objects of disability, and the possibility of independent participation. Notably, the meaning of assistive devices as progress, [dis]ability, and [poss]ability was equally evident as participants talked about mobility, everyday activities, and services. We discuss how considering [dis]ability as a discursive object in the situation might have enabled stroke survivors to participate.

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)0.016
metaresearch head score (Gemma)0.034
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0050.009
Scholarly communication0.0060.006
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.214
GPT teacher head0.440
Teacher spread0.226 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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