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Record W2284294525 · doi:10.20381/ruor-13144

Imagining Canada, imagining Canadians: National identity in English as a second language textbooks

2009· dissertation· en· W2284294525 on OpenAlexaboutno aff
Trevor Gulliver

Bibliographic record

VenueuO Research (University of Ottawa) · 2009
Typedissertation
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)LinguisticsNational identityGender studiesSociologyPolitical scienceArtAestheticsPoliticsPhilosophyLaw

Abstract

fetched live from OpenAlex

In this study, I establish that language textbooks are sites of discursive struggle through which nationalist imagined communities are reproduced. I use critical discourse analysis to analyze how these textbooks construct Canadian identities that position students in relation to an imagined community of Canada. I analyze twenty-four textbooks and three Citizenship and Immigration Canada publications used in government-funded language instruction in Ontario. Representations of Canada and Canadianness in the texts examined include and exclude student readers, participate in banal nationalism, and legitimate particular understandings of Canada. The identified textbooks mark nationality through flags, maps, references to nation, and the use of nation as a frame of reference. The textbooks also make claims about how 'Canadians' think and behave. This banal nationalism naturalizes and essentializes imaginings of 'Canada' and 'Canadianness' supporting particular and interested constructions and positive evaluations of 'Canadian' identity. Both government produced publications and identified textbooks legitimate constructions of Canadian identity through repeated positive representations of Canadianness; the marginalising inclusions of 'others'; the subordination of gendered, racialised, and classed social positions to nation; and by maintaining a low level of dialogicality overall.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.632
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.394
Teacher spread0.338 · 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 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

Citations4
Published2009
Admission routes1
Has abstractyes

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