Moving Asian American History from the Margins to the Middle in Elementary Social Studies Classrooms
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".