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Record W2978860005 · doi:10.5507/euj.2019.010

An evaluation of social media images portrayal of disability discourse: #amputeefitness

2019· article· en· W2978860005 on OpenAlexaff
Fallon R. Mitchell, Sara Santarossa, Isuri L. Ramawickrama, Emily F. Rankin, Jessica A. Yaciuk, Erin R. McMahon, Paula M. van Wyk

Bibliographic record

VenueEuropean Journal of Adapted Physical Activity · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsConversationFocus (optics)Social mediaPsychologyDiscourse analysisSociologyFocus groupSocial psychologyLinguisticsComputer scienceCommunicationWorld Wide Web

Abstract

fetched live from OpenAlex

The widespread use and influential impact of social media has made it increasingly important to analyze the social norms and discourses that are being presented and promoted among social media users. Thus, the purpose of this study was to examine whether the conversation and images associated with #amputeefitness on Instagram promoted the medical model discourse (i.e., a focus on aesthetic) or the social model of disability discourse (i.e., a focus on adapted physical activity). Using the Netlytic software, all publicly available Instagram media tagged with #amputeefitness were collected, and a text and image analysis were performed. The text analysis revealed that the conversation was positive or neutral and focused on four themes: community, motivation, aesthetic, and physical activity. The image analysis suggested an emphasis was placed on body structure (i.e., having an acceptable appearance) and physical activity. Although there was evidence of the medical model within the posts associated with #amputeefitness, there was a stronger indication of a shift towards the social model of disability discourse.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.049
GPT teacher head0.374
Teacher spread0.325 · 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 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

Citations12
Published2019
Admission routes1
Has abstractyes

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