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Record W4308963320 · doi:10.3138/chr-2021-0030

From a “Disciplined Intelligence” to a “Culture of Care”: Shifting Understandings of Emotions and Citizenship in Twentieth-Century Educational Discourses

2022· article· en· W4308963320 on OpenAlexvenueaboutno aff
Catherine Gidney

Bibliographic record

VenueCanadian Historical Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Education Studies Worldwide
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipSociologyActive citizenshipPersonalityPedagogyGood citizenshipAestheticsGender studiesPsychologyPolitical scienceSocial psychologyLawPolitics

Abstract

fetched live from OpenAlex

Attention to children’s emotional development in Canadian schools is often presented as a very recent concern. In fact, competing conceptions of students’ emotional well-being informed educational discourses throughout the twentieth century. This article examines educators’ changing ideals regarding good citizenship, particularly the affective attributes or expressions attached to those ideals, and argues that educational discourse has shifted from an emphasis on creating citizens with a “disciplined intelligence” to promoting a “culture of care.” The first half of the twentieth century witnessed the melding of a nineteenth-century Anglo-Christian moral imperative with a more modern emphasis on the search for personality. While this thrust lingered well into the century, educators increasingly embraced a culture of rights alongside evolving ideas about personality development from the 1950s to the 1980s. In the process, they prioritized self-expression or “self-actualization.” Beginning in the 1980s, educators transformed loose edicts about students’ personal fulfillment into more concrete pursuits of engaged pedagogy, active citizenship, and, increasingly, a “culture of care” within the classroom. In tracing these shifting emphases, this article highlights the need for critical analysis of the affective dimensions of education to better understand both the historic and current role of schools in shaping citizens.

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)0.000
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.974
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.347
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 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
Published2022
Admission routes2
Has abstractyes

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