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Record W2591204622

New Ecologies of the Real: Nonsimultaneity and Canadian Literature(s)

2016· article· en· W2591204622 on OpenAlexaffvenueabout
Winfried Siemerling

Bibliographic record

VenueStudies in Canadian Literature · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTemporalitiesSpatializationSociologyAgency (philosophy)EpistemologyAestheticsSocial scienceArtAnthropologyPolitical scienceLawPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

This essay draws attention to variations in the use of formal textual strategies that sometimes have been overlooked in the productive but potentially homogenizing shift from Canadian literature to Canadian literatures. A mere insistence on pluralization can run the risk of masking differences that include specific forms of “nonsimultaneity” or ungleichzeitigkeit (Ernst Bloch). Such differing relations to time—or heterochronicities— imply related, varying views of space, point to different functionalities of formal modes and genres, and influence how texts relate to audiences and intervene in the public sphere. I argue that the deployment of formal elements is often contingent on social dimensions and cultural specificity, and thus on contextual factors whose consideration was seen as detrimental to the discussion of Canadian literature in Frank Davey’s “Surviving the Paraphrase.” Focusing on examples drawn from black Canadian cultural expression, I examine contextually motivated temporalities in works by George Elliott Clarke, Marie-Celie Agnant, Sylvia Hamilton, Camille Turner, and Wayde Compton. By using distinct strategies of re-temporalization and re-spatialization, these writers and artists work towards the “not-yet” of a differently conceived future and exert civic agency with the help of formal choices in their art.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.835
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.020
GPT teacher head0.261
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 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 routes3
Has abstractyes

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