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Record W2463906698

"Two Blossoms," on a "Thistle-Stalk"

2016· article· en· W2463906698 on OpenAlexaff
Taylor Lemaire

Bibliographic record

VenueVerso: An Undergraduate Journal of Literary Criticism · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPoetryLiteratureFrench hornBeholdArtAestheticsArt historyHistorySociology
DOInot available

Abstract

fetched live from OpenAlex

From its opening mix of deliberately incongruous images (“mossy glens,” “substances like boils,” and “glowworm winks”) to its deft use of  Victorian and modern theories of the grotesque, to its innovative concluding gambit, Taylor LeMaire’s essay maps its own way through two exceptionally complex poems and the rich terrain of the aesthetics of the grotesque.  The essay is notably original first of all in comparing two classic Victorian texts very seldom considered in conjunction, though they were published within five years of each other. Secondly, LeMaire focuses not on the ugliness, lapses, and incongruity conventionally associated with the grotesque, but on its “positive” functions in both Browning’s “‘Childe Roland’” and Rossetti’s “Goblin Market.”  After a close and convincing analysis of strategically chosen textual details, combining consideration of poetic form with metaphoric and thematic content, LeMaire concludes with a surprising but apt shift from visual to sonic modes of the grotesque. She pairs the blast of the “slug-horn” that occurs at the end of Childe Roland’s quest when he reaches the “dark tower” with the animal-human goblin men’s seductive “cries” that ring enticingly in our ears in the opening of Rossetti’s poem. An exemplary essay. Whether one sound is ultimately more “positive” and beneficial than the other is left more open to question. As Browning’s Roland says of the mysterious appearance of the dark tower,  “solve it, you!” –Dr. Marjorie Stone

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.029
GPT teacher head0.270
Teacher spread0.241 · 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 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
Published2016
Admission routes1
Has abstractyes

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