Using Economic Analysis to Incorporate Reparations for Black Americans into the US History Classroom
Bibliographic record
Abstract
The United Nations recognizes that if a nation-state violates the civil rights of its citizens, it is responsible for providing reparations to the harmed group. Many Black Americans assert that the prolonged civil rights abuse they incurred during enslavement and post-slavery discrimination necessitate the US government provide reparations. Thus, many factions of the ongoing civil rights movement (Hall, 2005) focus on securing Black Americans' reparations. However, in my research, I found that US History standards and curricular resources often paint the civil rights movement as completed and reparations as a decontextualized political debate. This article encourages US History teachers to include reparations into their Civil Rights Movement instruction by incorporating economic analysis. To assist in this shift, I detail a framework that can support US History teachers in interweaving economic thinking and data to situate the topic of reparations into their Civil Rights curriculum.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 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".