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Record W4229049061 · doi:10.19068/jtel.2022.26.1.01

Canadian Culture, Identity, and Canadian Children’s Literature

2022· article· en· W4229049061 on OpenAlexaboutno aff
Sukjin Kang, Mi-Jin Ko, Jongwoo Lee, Gyu Han Kang

Bibliographic record

VenueThe Korean Society for Teaching English Literature · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismIndigenousGender studiesSociologyIdentity (music)Cultural identityValue (mathematics)Canadian literatureRegionalism (politics)Cultural pluralismPolitical scienceSocial sciencePoliticsAnthropologyLawAestheticsNegotiation

Abstract

fetched live from OpenAlex

This paper illuminates Canadian children’s literature in relation to Canadian cultural characteristics. Canadian children’s literature is a cultural product of Canada’s soil, reflecting and shaping the identity of Canadians. In other words, the questions of what Canadian culture is and what Canadian children’s literature is have been the main concerns of Canadians who have attempted to find and build value systems that differentiate themselves from Britain, France and the United States, By the end of the 20th century, regionalism and multiculturalism, in particular, emerged as the core values ​​of Canada. Canadian values ​​that respect the characteristics and diversity of each region and pursue the coexistence of different cultures are found in children’s literature, and such Canadian values ​​are firmly established through multiculturalism. Voices that had been neglected in the traditional discourses began to be expressed. Minorities, including oppressed women, indigenous peoples, and people with disabilities, expressed their positions more actively and created a new cultural landscape by publishing various texts, reflecting the way of thinking and emotions of minorities and their lives.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.187
Threshold uncertainty score0.943

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0120.024
Science and technology studies0.0390.018
Scholarly communication0.0150.003
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.007
GPT teacher head0.216
Teacher spread0.208 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Korean Society for Teaching English LiteratureSame topicThemes in Literature AnalysisFrench-language works237,207