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Record W4405380634 · doi:10.22439/asca.v56i2.7378

Let the Students Map Canadian Studies: Exploring Stereotypes of Canada

2024· article· en· W4405380634 on OpenAlexaboutno aff
Christophe Prémat

Bibliographic record

VenueAmerican Studies in Scandinavia · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceGeographyGender studiesSociology

Abstract

fetched live from OpenAlex

This article investigates the perceptions and stereotypes of Canada held by students in Nordic, Scandinavian, and Baltic countries participating in Canadian studies courses. Drawing upon eighty-seven papers submitted between 2021 and 2023, the study employs lexicometric analysis to discern recurring “othering” strategies employed by students. The interdisciplinary nature of the Canadian studies course, blending literature, history, and political science, aims to equip students with the knowledge necessary to examine the nuances of the Canadian social model. By examining cultural stereotypes, the study redefines Canadian studies as an integral component of (North) American studies, highlighting the importance of challenging initial representations and fostering critical thinking. Findings reveal students’ engagement in the process of othering, reflecting on Canadian identity, multiculturalism, and the integration of First Nations. The study underscores the significance of pedagogical interventions in creating spaces for transformation and critical reflection. Ultimately, it demonstrates the potential of area studies to assist students in structuring academic texts and encourages further exploration of themes related to memorial policies and reconciliation in courses on Canadian studies.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
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 head0.080
GPT teacher head0.360
Teacher spread0.280 · 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
Published2024
Admission routes1
Has abstractyes

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