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Record W4310153740 · doi:10.1093/fs/knac250

Absent Animals in Patrick Deville’s <i>Kampuchéa</i>

2022· article· en· W4310153740 on OpenAlexaff
Marla Epp

Bibliographic record

VenueFrench Studies · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicTravel Writing and Literature
Canadian institutionsMacEwan University
Fundersnot available
KeywordsNarrativeFace (sociological concept)AestheticsHistorySociologyPsychologyLiteratureArtSocial science

Abstract

fetched live from OpenAlex

Abstract This article focuses on the ways in which encounters with animals, a frequent trope in travel literature, are reworked in Patrick Deville’s Kampuchéa (2011) to reflect the current dire ecological situation. Deville’s narrator is in South East Asia following the path of French naturalist Henri Mouhot, whose diary of his travels was published in 1868. Although the travel routes are similar and the basic components of a travel narrative remain, Mouhot’s literary style is reconfigured to reflect the twenty-first-century traveller’s awareness of the violent past of the region and anxiety over the future of the planet. If animals abound in Mouhot’s diary, they are remarkable in Kampuchéa primarily through their absence. Deville does not, however, occlude them from his narrative, but rather writes about them in absentia . This article studies the implications of Deville’s writing about animals without any meaningful face-to-face encounters. It further considers the repercussions of these lost moments of exchange and argues that Deville’s commitment to writing about animals, even those who are absent, works to keep their looming extinction at the forefront of readers’ minds.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.007
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.052
GPT teacher head0.257
Teacher spread0.205 · 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
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

Citations0
Published2022
Admission routes1
Has abstractyes

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