Lessons Learned from Two Neighbors: How Educators Teach of United States Policies
Bibliographic record
Abstract
This study provides an analysis of data collected from Chihuahua, Mexico, and Ontario, Canada, educators on how United States (U. S.) policies are taught and discussed in their classrooms. Teachers and administrators were interviewed with regard to their respective curricula and classroom discussions. The researcher sought to gain insight on how historical and current U. S. policies are addressed. Participants responded to questions regarding how much time was devoted to U. S. policies in classroom discussions, how much open discourse exists in classrooms, what ideological differences are evident, and why Americans should be informed of perspectives in another country’s social studies classrooms. The researcher uses border pedagogy and meliorism to analyze how educators present geographic, historic, socioeconomic, and political issues as they relate to U. S. classrooms. Addressed are implications for integrating perspectives in U. S. classroom discussions and, in turn, broadening the social studies curriculum in American schools. Moreover, this study seeks to provide additional insight for those who educate on common issues in U. S. classrooms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".