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Record W2593467951 · doi:10.26522/ssj.v10i2.1421

Generating Ambivalence: Media Representations of Canadian Transplant Tourism

2016· article· en· W2593467951 on OpenAlexaffvenueabout
Lindsey McKay

Bibliographic record

VenueStudies in Social Justice · 2016
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsBrock University
Fundersnot available
KeywordsTourismMainstreamAmbivalenceOrgan donationScarcityOrgan transplantationNewspaperRepresentation (politics)TransplantationPublic relationsSociologyPolitical sciencePsychologySocial psychologyMedicineLawEconomics

Abstract

fetched live from OpenAlex

This article addresses transplant tourism as one facet of the international organ trade. It asks whether mainstream media portrayals of Canadian transplant tourist journeys convey messages supportive of stronger efforts to stop extra-territorial organ purchase. A postcolonial theoretical approach using Mary Louise Pratt’s study of travel writing is employed to conduct a discourse analysis of Canadian media and cultural representation from 1988 to 2015. The public learns that transplant tourism is “bad” but understandable, and either not our problem or a symptom of another problem. Three forms this message takes are: the broader organ trade is a distant and insurmountable problem; transplant tourists are innocent victims; and, resolution of a larger, national organ scarcity problem will end transplant tourism. I conclude that the media generates ambivalence towards the issue of transplant tourism. Reader attention is drawn away from health outcomes and human rights, especially of organ providers – reasons Canada might do more to stop transplant tourism – towards the challenges faced by transplant tourists, with the effect of eclipsing public discussion of whether and how to stop Canadians from buying organs in other countries.

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)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.403

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.006
Science and technology studies0.0350.023
Scholarly communication0.0150.005
Open science0.0010.008
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.061
GPT teacher head0.359
Teacher spread0.298 · 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 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

Citations3
Published2016
Admission routes3
Has abstractyes

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