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Record W2799507788 · doi:10.3390/genealogy2030022

Heritage Ethics and Human Rights of the Dead

2018· article· en· W2799507788 on OpenAlexaffabout
Kelsey Perreault

Bibliographic record

VenueGenealogy · 2018
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsCarleton University
Fundersnot available
KeywordsHumanityHonourIndigenousEnvironmental ethicsLawSociologyCzechGlobePolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Thomas Laqueur argues that the work of the dead is carried out through the living and through those who remember, honour, and mourn. Further, he maintains that the brutal or careless disposal of the corpse “is an attack of extreme violence”. To treat the dead body as if it does not matter or as if it were ordinary organic matter would be to deny its humanity. From Laqueur’s point of view, it is inferred that the dead are believed to have rights and dignities that are upheld through the rituals, practices, and beliefs of the living. The dead have always held a place in the space of the living, whether that space has been material and visible, or intangible and out of sight. This paper considers ossuaries as a key site for investigating the relationships between the living and dead. Holding the bones of hundreds or even thousands of bodies, ossuaries represent an important tradition in the cultural history of the dead. Ossuaries are culturally constituted and have taken many forms across the globe, although this research focuses predominantly on Western European ossuary practices and North American Indigenous ossuaries. This paper will examine two case studies, the Sedlec Ossuary (Kutna Hora, Czech Republic) and Taber Hill Ossuary (Toronto, ON, Canada), to think through the rights of the dead at heritage sites.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.084
Scholarly communication0.0070.005
Open science0.0010.005
Research integrity0.0030.003
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 head0.072
GPT teacher head0.360
Teacher spread0.288 · 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 designTheoretical or conceptual
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

Citations4
Published2018
Admission routes2
Has abstractyes

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