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Record W3130652571 · doi:10.29173/isotl530

Negotiating the Nation Through Superheroes

2021· article· en· W3130652571 on OpenAlexaffvenueabout
Lee Easton, Kelly Hewson

Bibliographic record

VenueImagining SoTL · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsMount Royal University
Fundersnot available
KeywordsVisionNegotiationNationalismIdeologyMulticulturalismRhetorical questionMedia studiesReading (process)SociologyPolitical scienceAestheticsArtLiteratureLawPoliticsSocial scienceAnthropology

Abstract

fetched live from OpenAlex

This case study focuses on Canadian students’ responses to our invitation to imagine their own nationalist superheroes whose costumes and powers represent a nation. We provide a close reading of 34 student artifacts to show how they draw on discourses that position Canada as a benevolent, multicultural country—a rhetorical formation we call the Canadian Shield. We also reveal how some artifacts negotiate tropes of the Shield, adapting or revising them in distinctive ways. We conclude, however, that when invited to create Canadian superheroes, many of the student creations reaffirm dominant visions of the country, and such habits of thought, we venture, are best considered as ideological bottlenecks.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.368
Threshold uncertainty score0.731

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0210.023
Scholarly communication0.0070.004
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.175
GPT teacher head0.418
Teacher spread0.243 · 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 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

Citations2
Published2021
Admission routes3
Has abstractyes

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