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Record W2903699631 · doi:10.7575/aiac.alls.v.9n.5p.49

An Experimental Documentary: Making a Case for Baird’s Modernism

2018· article· en· W2903699631 on OpenAlexaboutno aff
Ademolawa Michael Adedipe

Bibliographic record

VenueAdvances in Language and Literary Studies · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicPoetry Analysis and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsModernism (music)PostmodernismCriticismWatsonNarrativeAssertionFace (sociological concept)SociologyLiteratureAestheticsHistoryArt historyArtSocial science

Abstract

fetched live from OpenAlex

The refutation and the obliteration of the modernist era in Canadian literature by Robert Kroetsch and reasserted by Glen Wilmott makes it imperative to look at highly experimental literary works in the first half of the 20th century in Canada. The purpose of this paper, thus is to make a case for the inclusion Irene Bird’s Waste Heritage in the repertoire of modernist works in North America. The various criticism of Canadian literature as not having a modernist era needs to be debunked. The false assertion that Canadian literature moved straight from the Victorian era to a postmodernist face is probably due to the difficulty of defining what modernism is. The evolution and the expansion of the term modernism makes it imperative for one to reappraise the creative works of Irene Bird (Waste Heritage) and Sheila Watson (Double Hook) as modernist. An attempt to include Waste Heritage in the new modernist discourse of global literature by looking at the experimental way by which Baird used documentary modernism. The sustainability of a growing modern society vis-à-vis modernism, and the resistance of capitalism in Baird’s narrative would be used to make a case for Baird’s modernism.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.251
Threshold uncertainty score0.500

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0160.081
Scholarly communication0.0140.010
Open science0.0030.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.031
GPT teacher head0.377
Teacher spread0.347 · 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 source (direct Gemma or distilled Codex), 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
Published2018
Admission routes1
Has abstractyes

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