MétaCan
Menu
Back to cohort
Record W4315558631 · doi:10.3828/jlcds.2022.31

Beyond the Blues

2022· article· en· W4315558631 on OpenAlexaff
James Deaville, Chantal Lemire

Bibliographic record

VenueJournal of Literary & Cultural Disability Studies · 2022
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsUniversity of CalgaryCarleton University
Fundersnot available
KeywordsBluesNarrativePsychologyMental healthNormativeMusicalMental distressCognitive dissonanceAestheticsAdvertisingSocial psychologySociologyArtPolitical sciencePsychotherapistVisual artsLiterature

Abstract

fetched live from OpenAlex

The article examines the soundtracks of audiovisual commercials for antidepressants, investigating the musical practices of advertising for the pharmaceutical industry. After introducing the topic of direct-to-consumer medication advertising and its regulation in the United States through the concept of “fair balance,” the article considers the role of music in direct-to-consumer (DTC) commercials. The concepts of congruence and incongruence between visual, narrational, and musical elements are presented, in application to the soundtracks of antidepressant commercials, with detailed multimodal discourse analyses of commercials for the prescription medications Zoloft, Cymbalta, and Latuda. Studying their audiovisual narratives and elements leads to the conclusion that music serves as a crucial yet hidden suasive component in corporate campaigns that strategically target people who experience mental distress. To the extent that the commercials present normative states of mind and lifestyles as ideals for people with depression, they can be understood as coercive and as a result, subjects for critical unpacking through Mad studies, particularly in relation to their role in perpetuating sanist narratives and a biomedical model of mental health.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.558
GPT teacher head0.600
Teacher spread0.042 · 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.

Study designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Literary & Cultural Disability StudiesSame topicPharmaceutical industry and healthcareFrench-language works237,207