Racial and Ethnic Representation in Literature Taught in US High Schools
Bibliographic record
Abstract
We quantify the representation, or presence, of characters of color in English Language Arts (ELA) instruction in the United States to better understand possible racial/ethnic emphases and gaps in literary curricula. We contribute two datasets: the first consists of books listed in widely-adopted Advanced Placement (AP) Literature & Composition exams, and the second is a set of books taught by teachers surveyed from schools with substantial Black and Hispanic student populations. In addition to these book lists, we provide an unprecedented collection of hand-annotated sociodemographic labels of not only literary authors, but also their characters. We use computational methods to measure all main characters’ presence through three distinct and nuanced metrics: frequency, narrative perspective, and burstiness. Our annotations and measurements show that the sociodemographic composition of characters in books recommended by AP Literature has not shifted much for over twenty years. As a case study of how ELA curricula may deviate from the curricula prescribed by AP, our teacher-provided sample shows a greater emphasis on books featuring first-person, primary characters of color. We also find that only a few books in either dataset feature both White main characters and main characters of color. Arguably, these books may uphold a view of racial/ethnic segregation as a societal norm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".