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Record W2329607614 · doi:10.1386/ctl.7.2.143_1

Treaty education for ethically engaged citizenship: Settler identities, historical consciousness and the need for reconciliation

2012· article· en· W2329607614 on OpenAlexaffabout
Jennifer Tupper

Bibliographic record

VenueCitizenship Teaching and Learning · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsCitizenshipTreatySociologyLawDemocracyEconomic JusticeArgument (complex analysis)ConsciousnessValue (mathematics)MythologyPolitical scienceEnvironmental ethicsEpistemologyPolitics

Abstract

fetched live from OpenAlex

This article explores the possibilities of treaty education for reconciliation with First Nations people, as corrective to the foundational myth of Canada and as a means of fostering ethically engaged citizenship. Lack of historical understanding demonstrated by Canadians regarding treaties and the treaty relationship is examined in relation to discourses of liberal democratic citizenship. Drawing on ‘remembrance as a source of radical renewal’ ‘ethical relationality’ and ‘justice-oriented citizenship’, the argument is made that treaty education has the potential to help all students learn from and through events and experiences of the past in ways that inform not only their historical consciousness, but their dispositions as Canadian citizens, and their relationships with one another. While the discussion in this article is specific to treaty education, it is relevant to broader conversations about the role and value of including more diverse stories/experiences in national histories. Throughout the discussion, attention is paid to the interconnections of citizenship and history education, particularly with respect to possibilities for engaging differently in the world, alongside one another, politically, socially, culturally and ethically as part of the necessary and urgent process of reconciliation.

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)0.018
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0240.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.316
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.

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

Citations29
Published2012
Admission routes2
Has abstractyes

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