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Record W7133396777

Review of Peter Narvaez, 'Of Corpse: Death and Humor in Folklore and Popular Culture': Logan Utah, Utah State University Press, 2003. $US24.95. ISBN 0874215595 (paper)

2004· review· en· W7133396777 on OpenAlexaboutno aff

Bibliographic record

VenueRUNE (Research UNE) · 2004
Typereview
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFolkloreSeriousnessMacabreAmateurAfterlifeState (computer science)Laughter
DOInot available

Abstract

fetched live from OpenAlex

Laughter is often somewhat malicious, being concerned with the fate of other people and their discomforts, incongruous experiences or at some (displayed) awkwardness. This collection of eleven papers is largely concerned with the grim humour or the macabre that is often associated with death and with the sudden (fresh) perception of the incongruous fact of one's mortality. The watching /reflective individual thereby becomes defensive, embarrassed and infinitely more self-aware.For all these papers explore aspects of the seriousness of the convergence between death and of the related mood of grim humour. While the editor's concerns began with his approach to the 'merry wake' in Newfoundland, and to losses of life at sea, he soon came to categorise such stories by the concept of 'religious fatalism', or the deeper understanding of one's frail personal mortality, in short, by a mood that is a varying mix of the serious and the ludic. Thus it - the book- joins a growing number of probing folklore studies from c. 1980 that focus on private and public traditions of death, many concerned - like contemporary legends - with the rapid social changes that have taken place in American culture in that period. The prevailing tones of these collections - and the present one - cover the full gamut from the mild and gentle to the magical, the deeply religious, the tragic and those concerned with specific spaces and with the processes and places of internment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.852
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.080
GPT teacher head0.330
Teacher spread0.250 · 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
GenreReview

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
Published2004
Admission routes1
Has abstractyes

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