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Record W4313489948 · doi:10.19068/jtel.2022.26.3.05

Canadian Science Fiction for Children and Young Adults: Focusing on Novels from the 1980s

2022· article· en· W4313489948 on OpenAlexaboutno aff

Bibliographic record

VenueThe Korean Society for Teaching English Literature · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFantasyMainstreamMulticulturalismDistrustHarmony (color)AdventureOriginalityIdentity (music)Techno-thrillerSociologyDiversity (politics)AestheticsHistoryLiteratureLiterary fictionSocial sciencePsychologyPolitical scienceArtLiterary criticismAnthropologyArt historyVisual arts

Abstract

fetched live from OpenAlex

The present study overviews Canadian science fiction for children and young adults in its early history. Canada’s multiculturalism is a great resource for diversity on their literary works, but at the same time, it often turns into concerns on their national identity. Canadian novels portray this unique trait in their stories with three major features. By contrasting the technology-dominated society with the nature-friendly one, they ultimately aim for an idyllic society. Also, the works express distrust of technology and progress with concerns about negative effects on the global environment. Finally, they lie on the blurred border between fantasy adventure and science fiction. Unlike mainstream science fiction novels, Canadian children’s SF writers take the subjects of science, nature, and humans more seriously. Depicting a variety of possible future societies, they continue to emphasize both the harmony of technology and the nature and the exploration of human identity. This originality distinguishes them from other countries’ works and are sufficiently attractive to many young readers.

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)0.002
metaresearch head score (Gemma)0.005
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: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.428

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.008
Science and technology studies0.0230.008
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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