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Record W2767175454 · doi:10.3138/jcs.51.1.90

Creating Canada: Education for Inclusion or Different Versions of Colonial Stories?

2017· article· en· W2767175454 on OpenAlexvenueaboutno aff
Krysta Pandolfi, Carl E. James

Bibliographic record

VenueJournal of Canadian Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeMulticulturalismTrope (literature)SociologyGender studiesColonialismScotsInclusion (mineral)IndividualismRacismBiculturalismHistoryMedia studiesLiteraturePolitical scienceLawArt

Abstract

fetched live from OpenAlex

In 2006, Dragon Hill Publishing released a book (the first of the series) titled How the Scots Created Canada. Since then, it has released six additional books detailing how the groups they identify as Italian, French, English, Black, Chinese, and Polish have each “created” Canada. Dragon Hill claims that their aim “is to produce and market books to the popular adult and youth markets … that will help individuals improve their self-image and that will help increase understanding and tolerance among diverse individuals and cultures.” In this article we examine how, rooted in the Canadian multicultural discourse, these “count me in” narratives of the featured ethnically and racially minoritized Canadians are presented as “add-ons” rather than integrated into the “Canadian narrative.” We explore how attempts at inclusive education and the quest to dispel, for some ethnic groups, the perpetual foreigner trope re-inscribe an uncritical embrace of Western European narratives based on discourses of whiteness, individualism, and conquest. Employing critical theories relating to decolonization, we highlight how the “counter-narratives” presented in the series serve to accommodate and simultaneously become complicit in “creating” problematic, incomplete, contradictory, and misrepresentative narratives of Canada and the featured minoritized groups.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
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 head0.061
GPT teacher head0.375
Teacher spread0.314 · 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
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

Citations6
Published2017
Admission routes2
Has abstractyes

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