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Record W3011472816

An Exploration of Death Cafés in Canada

2018· dissertation· en· W3011472816 on OpenAlexaboutno aff
Miriam Karrel

Bibliographic record

VenueMacSphere (McMaster University) · 2018
Typedissertation
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicine
DOInot available

Abstract

fetched live from OpenAlex

Do people talk about death? Many scholars argue that people in our society do not talk about death; that it is taboo, it is denied or sequestered into hospitals and funeral homes and exists outside of everyday life. And yet, others argue that death is “a very badly kept secret” with hundreds of books published on the topic in the last few decades, most of them claiming that we cannot talk about death. This disconnect leads some to argue that there is a revival of death happening instead. My research sits at the nexus of this tension; I attended death cafés around Southern Ontario to explore the dialogues that emerge in spaces set out to break the presumed taboo around death. At a death café people are meant to “drink tea, eat cake, and talk about death.” The objective of these events is “to increase awareness of death with a view to helping people make the most of their finite lives.” This statement, from the official death café website, assumes that facing death will help to make sense of, and give perspective to, life. I explore how and if death cafés accomplish their intended purpose of encouraging existential discussion, and if such a discussion was in fact beneficial to the attendees. I argue that the discussions at the death cafés I attended did not seem to fulfill the purpose stated on the website of encouraging existential discussion about one’s own death. I then situate this observation in the context of broader understandings of the denial of death thesis generally and in terms of residual Victorian romanticism and attachment to others.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.783
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0610.000

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 head0.035
GPT teacher head0.284
Teacher spread0.249 · 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 designOther design
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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