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Record W2312408411 · doi:10.3138/ecf.28.3.473

The Sentimental Virtuoso: Collecting Feeling in Henry Mackenzie’s <i>The Man of Feeling</i>

2016· article· en· W2312408411 on OpenAlexvenueno aff
Barbara M. Benedict

Bibliographic record

VenueEighteenth-Century Fiction · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicLiterature: history, themes, analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingNarrativePathosLiteratureCharacter (mathematics)PraiseArtAestheticsPsychologySocial psychology

Abstract

fetched live from OpenAlex

This article explains the ambiguities in Henry Mackenzie’s quasi-ironic sentimental novel, The Man of Feeling, by examining its debt to an earlier, formative literary tradition: the seventeenth-century character collection that features the caricatured antiquarian virtuoso. Character collections, exemplified by Samuel Butler’s Characters (mainly written between 1667 and 1669), constitute catalogues of ridiculed social and psychological types, prominent among whom are collector-characters derogated for antisocial self-absorption, arrogance, scopophilia, impotence, and credulity. As a sentimental novel, written in an era that highly valued sociability, The Man of Feeling reveals how this satiric inheritance com plicates the praise of feeling. It reworks the structure and types of the character tradition and the figure of the antiquarian virtuoso by means of narrative frames that distance readers from the sen ti mental incidents; an episodic form that fractures sequential narrative; and rhetoric, themes, and characters that play on the opposition between materiality, idea, and feeling that informs the caricature of the antiquarian virtuoso. These features help to explain the ambiguity of the “man of feeling”: the sentimental virtuoso who both objectifies and personalizes a world of collectible experiences.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.212
Teacher spread0.198 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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