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Record W2502291013 · doi:10.1057/9781137065995_6

The Last Resource: Witnessing the Cannibal Scene

2012· book-chapter· en· W2502291013 on OpenAlexaboutno aff
Heather Davis-Fisch

Bibliographic record

VenuePalgrave Macmillan US eBooks · 2012
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHistoryAngerOutrageNewspaperNarrativeCriticismSadnessLiteratureGenealogyArtLawMedia studiesPsychologySociologyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

In October 1854, Dr. John Rae returned from the Arctic telling the chilling story that members of the Franklin expedition had resorted to cannibalism in their final days, mutilating the bodies of their dead companions and cooking their remains in kettles. The story, which Rae heard from Inuit who had been passing it along for at least four years, generated anger and sadness among the missing men’s families, heated denials and awkward compensatory narratives in daily newspapers, and hysterical racist invectives in Charles Dickens’s weekly periodical Household Words. Public responses to Rae’s report included anger with the Admiralty for not investigating the King William Island area earlier, criticism of Rae for returning home rather than traveling to the area himself, and logistical questions concerning how the ships had been lost and the primary causes of death. In general, the public accepted that Franklin and his men were long dead, in large part because Rae had returned with personal artifacts that undoubtedly belonged to the missing men. The allegations of cannibalism, however, lead many to question whether Inuit were trustworthy witnesses and whether they accurately understood the remains they found.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0170.013
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.019
GPT teacher head0.262
Teacher spread0.243 · 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 designNot applicable
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
Published2012
Admission routes1
Has abstractyes

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