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Record W4200246430 · doi:10.29173/assert24

Moving Asian American History from the Margins to the Middle in Elementary Social Studies Classrooms

2021· article· en· W4200246430 on OpenAlexvenueno aff
Noreen Naseem Rodríguez

Bibliographic record

VenueAnnals of Social Studies Education Research for Teachers · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipAgency (philosophy)DemocracyAsian americansSocial studiesGender studiesSociologyCitizenship educationPedagogyVotingPolitical scienceSocial scienceLawAnthropologyEthnic groupPolitics

Abstract

fetched live from OpenAlex

This article describes how three Asian American elementary teachers in Texas reflected on the absence of Asian American histories in their own educational experiences, which later inspired them to teach Asian American histories in their classrooms. The teachers’ lessons about Asian American history required them to first (re)define the term Asian American with their students, and the teachers also (re)defined what it meant to be American. Ultimately, they promoted cultural citizenship, which is more inclusive and critical than traditional forms of citizenship that are defined by individual acts like voting and following rules. Cultural citizenship promotes difference as a resource; emphasizes the need to respect and humanize others; includes the voices, experiences, and perspectives of People of Color; and emphasizes human rights and agency. Asian American children’s literature was an essential tool in disrupting exclusionary histories and notions of citizenship as equal to whiteness, and the teachers' work demonstrates how educators can move Asian Americans from the margins to the middle of social studies instruction to support better teaching of U.S. history and democracy.

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)0.004
metaresearch head score (Gemma)0.005
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.535
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.630
GPT teacher head0.568
Teacher spread0.061 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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