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Record W3089608815 · doi:10.16995/pr.3209

‘the words I taught to him’: Interfusional Language Play and Brian O’Nolan’s ‘Revenge on the English’

2016· article· en· W3089608815 on OpenAlexaff
Joseph LaBine

Bibliographic record

VenueThe Parish Review Journal of Flann O Brien Studies · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Art and Culture Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIrishIntertextualityTheme (computing)SwiftComicsReading (process)LiteratureHAMLET (protein complex)HybridityArtArgument (complex analysis)LinguisticsHistoryPhilosophyComputer scienceChemistry

Abstract

fetched live from OpenAlex

This article&nbsp;discusses the interlingual and cross-cultural resonances of the Irish language in the English and French intertextuality of O’Nolan’s short fiction and column writing through the coordinate of 'interfusionality.' The argument follows two interrelated strands: the first addresses the language theories expressed in O'Nolan's short story ‘Díoghaltais ar Ghallaibh ’sa Bhliain 2032!,’ published in <i>The Irish Press</i> in 1932, and the second traces this hybridity in the first <i>Cruiskeen Lawn</i> column through a compartive reading of Myles and 'An Broc' with Alphonse Allais, Edward Lear, and Stephen Leacock. The aim is to show that these works share a common theme wherein O’Nolan satirises Ireland’s language debate by employing hybridised language and projecting either the growth or destruction of Irish within the comic arc of satire. <b>To read the article, click Download or View PDF.</b><br>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.604
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.037
GPT teacher head0.284
Teacher spread0.247 · 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 designNot applicable
Domainnot available
GenreReview

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

Explore more

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