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Record W2148176058 · doi:10.1136/jme.2007.020776

Ordering suicide: media reporting of family assisted suicide in Britain

2007· article· en· W2148176058 on OpenAlexaff
Albert Banerjee, Daphna Birenbaum‐Carmeli

Bibliographic record

VenueJournal of Medical Ethics · 2007
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsYork University
Fundersnot available
KeywordsNewspaperAssisted suicideContradictionPsychologyMedicineLawPsychiatryPolitical scienceEpistemology

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore the relationship between the presentation of suffering and support for euthanasia in the British news media. METHOD: Data was retrieved by searching the British newspaper database LexisNexis from 1996 to 2000. Twenty-nine articles covering three cases of family assisted suicide (FAS) were found. Presentations of suffering were analysed employing Heidegger's distinction between technological ordering and poetic revealing. FINDINGS: With few exceptions, the press constructed the complex terrain of FAS as an orderly or orderable performance. This was enabled by containing the contradictions of FAS through a number of journalistic strategies: treating degenerative dying as an aberrant condition, smoothing over botched attempts, locating the object of ethical evaluation in persons, not contexts, abbreviating the decision making process, constructing community consensus and marginalising opposing views. CONCLUSION: The findings of this study support the view that news reporting of FAS is not neutral or inconsequential. In particular, those reports presenting FAS as an orderly, rational performance were biased in favor of technical solutions by way of the legalisation of euthanasia and/or the involvement of medical professionals. In contrast, while news reports sensitive to contradiction did not necessarily oppose euthanasia, they were less inclined to overtly support technical solutions, recognising the importance of a trial to address the complexity of FAS.

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)0.028
metaresearch head score (Gemma)0.178
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.178
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.421
GPT teacher head0.538
Teacher spread0.117 · 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 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
Published2007
Admission routes1
Has abstractyes

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