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Record W2958381932

Words as predators in Henry James's The wings of the dove

2000· article· en· W2958381932 on OpenAlexvenueno aff
Gail Trussler

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2000
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDovePredationArtBiologyEcologyLawPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The frequent predatory images in 'The Wings of the Dove' are created by the characters in order to reconcile the conflict they experience between their strong desires for personal gain and their equally strong desires to see themselves in a favourable light. In portraying themselves as victims of other characters' predation, the characters conveniently overlook the rapacious nature of their own behaviour, while at the same time experiencing a sense of control over the characters around them by capturing them with verbal images. The roles of prey and predator within the novel are thus not fixed; they fluctuate according to the motivations of the characters who produce the images, and are paradoxically overturned by the predatory power these characters gain in the very act of defining themselves as prey. The ability of words to capture and to create a sense of control makes them a powerful commodity in the novel, and yet their power frequently eludes the characters as they attempt to grasp it. Words to a certain extent parallel money, and the characters' comprehension and utilisation of the power of words to limit or to expand meaning is affected by the limitations, or lack thereof, of their own wealth. Words, however, are shown to have a power more far-reaching and potentially sinister than that of money, for their implications are ontological; in representing a person, they serve as a replacement for the person, and so figuratively annihilate them.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.003
GPT teacher head0.149
Teacher spread0.146 · 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.

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

Citations0
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueLibrary and Archives Canada (Government of Canada)Same topicDiscourse Analysis in Language StudiesFrench-language works237,207