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Record W4391862713 · doi:10.51644/9781554581030-001

Preface

2007· book-chapter· en· W4391862713 on OpenAlexaboutno aff
Smaro Kamboureli

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsPhilosophy

Abstract

fetched live from OpenAlex

anadian literature: a construct bounded by the nation, a cultural byproduct of the Cold War era, a nationalist discourse with its roots in colonial legacies, a literature that has assumed transnational and global currency, a tradition often marked by uncertainty about its value and relevance, a corpus of texts in which, albeit not without anxiety and resistance, spaces have been made for First Nations and diasporic voices.These are some of the critical assumptions scholars have brought to the study of CanLit, as we have come to call it for the sake of brevity, but also affectionately, and often ironically as we recognize the dissonances inscribed in the economy of this term.Whether it is considered an integral part of the Canadian nation formation, an autonomous body of works, a literature belonging somewhere between nation and literariness, or a part of "world literature," CanLit has been subject to a relentless process of institutionalization. Sometimes subtly, sometimes crudely, it has always been employed as an instrument-cultural, intellectual, political, federalist, and capitalist-to advance causes and interests that now complement, now resist, each other.This is not a process peculiar to CanLit.From the literary traditions of Germany and France to those of Brazil, India, and Australia, literature has been mobilized as a discourse that, no matter the diversity of its particular aesthetic and formal configurations, has served the geopolitical and sociocultural ends of institutions that are often at odds with what it sets out to SMARO KAMBOURELI

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.718
Threshold uncertainty score0.909

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)0.0920.001

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 head0.048
GPT teacher head0.243
Teacher spread0.196 · 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
GenreOther

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

Citations5
Published2007
Admission routes1
Has abstractyes

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