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Record W2561008083 · doi:10.1080/14992027.2016.1265155

Representations of workers with hearing loss in Canadian newspapers: a thematic analysis

2016· article· en· W2561008083 on OpenAlexaffabout
Raphaelle Koerber, Mary Beth Jennings, Lynn Shaw, Margaret F. Cheesman

Bibliographic record

VenueInternational Journal of Audiology · 2016
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsDalhousie UniversityWestern University
Fundersnot available
KeywordsNewspaperAudiologyHearing lossThematic analysisThematic mapPsychologyLinguisticsMedicineGeographySociologyMedia studiesCartographyQualitative research

Abstract

fetched live from OpenAlex

OBJECTIVE: Participation in the labour force with a hearing impairment presents a number of challenges. This study describes how Canadian newspapers represent workers with hearing loss. DESIGN: Taking a critical framing theory approach, thematic analysis was performed through coding relevant articles, abstracting and hierarchically categorising themes. STUDY SAMPLE: Seven English-language Canadian newspapers were searched for publications between 1995 and 2016. Twenty-six articles met our criteria: discussing paid workers with hearing loss who used English rather than sign language on the job and making reference to workers' competence. RESULTS: We identified a global theme, Focussing on a good worklife or focussing on a limited worklife, composed of three organising themes (1) Prominent individuals struggle, take action, and continue despite hearing loss, (2) Workers with hearing loss in the community create their best day themselves, and (3) Workers with hearing loss, as a generalised whole, are portrayed as either competent or limited. CONCLUSIONS: The dominant framing portrays individual workers as ingenious, determined, and successful. Negative framings were predominantly generalisations to these workers as a group. To generate more positive framings, professionals can build relationships with consumer groups and, when contacted by the media, direct journalists to interview workers with hearing loss.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.363
Teacher spread0.332 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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