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Record W2157626686 · doi:10.1177/1746197913483657

Beyond patriotic education: Locating the place of nationalism in the public school curriculum

2013· article· en· W2157626686 on OpenAlexaff
Kevin McDonough, Andrée‐Anne Cormier

Bibliographic record

VenueEducation Citizenship and Social Justice · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Education and Schools
Canadian institutionsUniversité de MontréalMcGill University
Fundersnot available
KeywordsPatriotismNationalismSociologyNormativePoliticsDemocracyCivic virtueCosmopolitanismEpistemologyPolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

The main thesis we want to defend in this article is that learning about nationalism from a historical, sociological, and normative point of view constitutes one important, but rather neglected, dimension of a good citizenship education. Although the debate about nationalism and education has received considerable attention from political and educational philosophers in recent years, the dispute has mainly focused on the question of whether public schools can legitimately promote nationalist sentiments, that is, patriotism. However, in this article, we wish to shift the focus away from the question of promoting patriotism and toward the question of the role that teaching about the phenomenon of nationalism and about specific nationalist movements can play in reinforcing liberal and democratic civic values and principles. We argue that such teaching can indeed play an important role and that this is true regardless of whether one views patriotism as a civic virtue or not and regardless of whether the aim of promoting patriotism in schools is legitimate or not.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.017
Scholarly communication0.0060.005
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.318
Teacher spread0.300 · 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

Citations27
Published2013
Admission routes1
Has abstractyes

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