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Between Every “Now” and “Then”: A Role for the Study of Historical Agency in History and Citizenship Education

2003· article· en· W2050907490 on OpenAlexaff
Kent den Heyer

Bibliographic record

VenueTheory & Research in Social Education · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAgency (philosophy)SociologyArgument (complex analysis)Social studiesEpistemologyCitizenshipSense of agencyPerspective (graphical)Causality (physics)Social psychologySocial sciencePsychologyPedagogyPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

This article reports on a review of research into students' reasoning about social change and causes they attribute to selected historical events. In this review, I distinguish studies into social change and causality as two methodological approaches to historical understanding before relating findings into the ways that students reason about agency in social change. I consider two of many possible explanations for these findings, one each from a cognitive and a social psychology perspective. I then turn to sociology for two articulations of agency as tools to enhance students' historical thinking and reflection on their variegated capacities as agents of social life: a) personal agency as nested moments of re-“iteration,” “practical evaluation,” and “projectivity” and b) historical agency as collectively expressed struggle over the ideals, images, and stories people use to reiterate a past in the present so as to imagine personal and social projects. I argue throughout that student attention in classrooms to assumptions about agents and agency used in historical explanations enhances both their historical explanations and capacities as citizens. Rather than citizens, however, I begin this article with a feminist argument that teachers address students first and foremost as agents.

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)0.009
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.317
GPT teacher head0.499
Teacher spread0.182 · 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 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

Citations48
Published2003
Admission routes1
Has abstractyes

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