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Record W6921844272 · doi:10.7939/r3-x32v-ac97

Cultural Mythology in Citizenship Education: The Case of Alberta/Canada

2022· dissertation· en· W6921844272 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2022
Typedissertation
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipMythologyIdeologyThe ImaginaryAmbivalenceGood citizenshipPresuppositionPower (physics)

Abstract

fetched live from OpenAlex

The social studies and history education research communities have paid a great deal of attention to citizenship and citizenship education. This dissertation, as both theoretical and empirical parts of that scholarly attention, delves deeper into the ways in which we attend to good citizenship in and for Alberta/Canada by focusing on two interrelated levels of education: a) the provincial education policy and curriculum contexts and b) the ways in which social studies and/or history teachers interpret and imagine good citizenship in and for Alberta/Canada. Based within my theoretical (re)configuration that provides a philosophical base of the key terms I use (i.e., ideology, historical-individual agency, imaginary, and cultural mythology), this dissertation offers a critical discourse analysis of the collected data sources from un/official documents for Alberta education and six experienced teachers. In so doing, this dissertation seeks to illustrate three critical points: (1) the contours of a dominant imaginary of Albertan/Canadian with its constitutive cultural mythology and their ontological and epistemological presuppositions that rest substantially upon particular ideologies (e.g., liberalism, (neoliberal) capitalism, and colonialism), (2) the ways in which cultural myths (e.g., diversity), as elements of a particular cultural mythology, both disguise and disseminate a monolithic and depoliticized version (and vision) of Canadian citizenship based within that dominant imaginary as neutral, legitimate, and universal, and (3) (social studies and/or history) teachers’ ongoing struggles that stem from their fraught and ambivalent relationships with that particular cultural mythology. With these illustrations, I attempt to elucidate not only unequal relations of power in that specific conception of citizenship and its undergirding ontological and epistemological beliefs that perpetuate systemic inequality and social discrimination in (but not limited to) Canadian society, but also teachers’ (and our) ongoing struggles over that specific conception of citizenship and identity that might inaugurate a springboard to dismantle a dominant imaginary of Albertan/Canadian with its constitutive cultural mythology. With all my effort to make sense of the ways in which we attend to good citizenship in and for Alberta/Canada, this dissertation strives to reveal cultural assumptions and biases regarding citizenship we as educators presume to teach. In doing so, I offer some important insights into the issues at the heart of the K-12 citizenship education in and beyond Canada germane to identity, citizenship, globalization, ideologies, and their entwined relationships. The value of doing so is not limited to disclosing current various educational issues and dynamics entwined with citizenship. Rather, the value in doing so is to provide curriculum scholars and teachers with the critical ways to think outside our inherited cultural biases about the (prevalent) meanings of citizenship and citizenship education. These critical ways, I believe, are essential to address unequal relations of power in such cultural biases and their undergirding ontological and epistemological beliefs, which is crucial to disrupt systemic inequality and social discrimination we all strive to resist.

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)0.005
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 score0.208
Threshold uncertainty score0.919

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0480.031
Scholarly communication0.0110.002
Open science0.0020.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.287
Teacher spread0.259 · 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

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