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Record W4388681254 · doi:10.19068/jtel.2023.27.2.01

Canada's Literary and Cultural Heritage and Yann Martel: Reading Canadian Identity in 101 Letters to a Prime Minister

2023· article· en· W4388681254 on OpenAlexaboutno aff
Sukjin Kang, Hee Jin Ro, Mi-Jin Ko, Jongwoo Lee, Gyu Han Kang

Bibliographic record

VenueThe Korean Society for Teaching English Literature · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicScottish History and National Identity
Canadian institutionsnot available
Fundersnot available
KeywordsPrime ministerIdentity (music)Reading (process)HistoryPrime (order theory)Cultural heritageCultural identityArt historyLiteratureSociologyArtAestheticsLawArchaeologyPolitical scienceSocial sciencePolitics

Abstract

fetched live from OpenAlex

This paper explores Canada’s literary and cultural heritage in Yann Matel’s 101 Letters to a Prime Minister, which has been largely ignored so far. Martel seems far removed from Canadianness, considering the diverse cultural backgrounds of his major works. However, he had an in-depth understanding of the intellectual traditions of Canada and its identity, along with a belief that Canada’s cultural heritage should be preserved and developed. This belief is condensed and well expressed in 101 Letters. This book demonstrates a wide spectrum of Canadianness. It covers the traditional conception of Northrop Frye’s educated imagination and the typical Canadian people’s voices, including Milton Acorn and Al Purdy. Alice Munro and Margaret Atwood depend less on regional characteristics, while John Steffler and Paul Quarrington are deeply involved in these features. Aboriginal and Quebecois writers such as Tomson Highway, Wajdi Mouawad, and Gabriel Roy complicate the conception of Canadianness, thus challenging a monotypical understanding of Canadian literary and cultural legacy. 101 Letters investigates a wide spectrum of Canadian cultural layers and discovers the chasms among them.

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)0.000
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: Empirical
Teacher disagreement score0.595
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueThe Korean Society for Teaching English LiteratureSame topicScottish History and National IdentityFrench-language works237,207