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Record W7064786142

Communicating contentious issues in Canada: Analyzing media discourse of medical assistance in dying (MAID)

2020· dissertation· en· W7064786142 on OpenAlexaffabout

Bibliographic record

VenueSummit (Simon Fraser University) · 2020
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsNucleofectionGestational periodTSG101HyporeflexiaDysgeusiaDiafiltrationPretextDemotionLiquation
DOInot available

Abstract

fetched live from OpenAlex

Examining the ways medical assistance in dying (MAID) discourse is presented in the media, this thesis analyzes the key themes, issues and contentions found throughout the topic in Canada's two national papers, The Globe and Mail and the National Post.Through a mixed-methods approach of qualitative and quantitative means, this study examines the period in and around June 17, 2016 when MAID legislation came into effect up to July 1, 2019 using a thematic content analysis, framing analysis, and sentiment analysis approach.Collectively, these methods allowed for an in-depth analysis and breakdown of the ethical, moral, religious, and personal beliefs that contribute to key contentions around the topic of MAID, supplemented by five in-depth interviews among individuals with vested interest in the subject matter.Together, these methods aimed to explore the way contentious issues are presented in the media in the context of medical assistance in dying.

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)0.011
metaresearch head score (Gemma)0.038
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 score0.113
Threshold uncertainty score0.816

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.038
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.011
Science and technology studies0.0340.016
Scholarly communication0.0170.004
Open science0.0020.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.262
Teacher spread0.246 · 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
Published2020
Admission routes2
Has abstractyes

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