MétaCan
Menu
Back to cohort
Record W4252813063 · doi:10.32920/ryerson.14652243

Seeking truths: ableist media representations and Toronto’s youth

2021· preprint· en· W4252813063 on OpenAlexaffabout
Kendra Joyce Belle

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsToronto Metropolitan UniversityCentre for Social Innovation
Fundersnot available
KeywordsMainstreamNarrativeThematic analysisAbleismPsychologyDisability studiesSociologySocial mediaNarrative inquiryGender studiesSocial psychologyQualitative researchPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This MRP examines the impact that ableist media representations have on youth living with disabilities in the GTA. More specifically, it seeks to answer three essential questions, (1) How have ableist representations of disability in media impacted the way youth with disabilities see themselves? (2) What representations of disability would youth with disabilities like to see in mainstream media? (3) What is social work’s role in changing these ableist media tropes and stereotypes? Using a narrative research methodology, this researcher collected the stories of three Torontonian youths between the ages of 18 to 29 years old, who self-identify as living with a disability. Episodic interviews and thematic data analysis were used to reveal several significant findings. Overall, participants felt that the media does not accurately represent their experiences of disability, often relying on stigmatizing stereotypes that influence their interactions with others, ultimately impacting the way they feel about themselves.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.670
Threshold uncertainty score0.656

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0110.012
Scholarly communication0.0080.003
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.059
GPT teacher head0.356
Teacher spread0.297 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicDisability Rights and RepresentationFrench-language works237,207