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Record W2148126927 · doi:10.3138/ecf.25.4.751

Natural History and Narrative Sympathy: The Children’s Animal Stories of Edward Augustus Kendall (1775/6?–1842)

2013· article· en· W2148126927 on OpenAlexvenueno aff
Jane Spencer

Bibliographic record

VenueEighteenth-Century Fiction · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsnot available
FundersArts and Humanities Research Council
KeywordsSympathyNarrativeSentienceFableEmpathyNaturalismFeelingLiteraturePsychologyRepresentation (politics)AestheticsPsychoanalysisHistoryArtEnvironmental ethicsPhilosophySocial psychologyEpistemology

Abstract

fetched live from OpenAlex

Edward Augustus Kendall (1775/6?–1842), a late eighteenth-century writer of children’s animal stories, deserves recognition for his sustained attempt to offer an empathetic rendition of imagined animal experience in fiction. His early fiction, including the dog story Keeper’s Travels (1798) and several tales of bird life, contributed to the development of more sympathetic attitudes to non-human animals in the late eighteenth century. Two factors influenced the development of Kendall’s innovative treatment of animal characters. First, the natural history of Buffon and his English translators and followers, in particular William Smellie, informed Kendall’s detailed attention to animal behaviour and to questions of animal mind and sentience. Second, the contemporary development of narrative techniques designed, on the basis of the imaginative sympathy theorized by Adam Smith, to encourage readers to identify with protagonists’ feelings, prompted Kendall to extend such methods to the representation of animal characters. He helped shift animal representation away from fable and satire towards naturalism and empathy. His use of third-person narrative proved most fruitful in this regard, and anticipated later developments in the imaginative apprehension of non-human experience.

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 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 score0.511
Threshold uncertainty score0.970

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.203
Teacher spread0.192 · 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.

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

Citations1
Published2013
Admission routes1
Has abstractyes

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