A Community College Instructor Like Me: Race and Ethnicity Interactions in the Classroom
Bibliographic record
Abstract
Detailed administrative data from a large and diverse community college are used to examine if academic performance depends on whether students are the same race or ethnicity as their instructors. To identify racial interactions and address many threats to internal validity we estimate models that include both student and classroom fixed effects. Given the large sample sizes and computational complexity of the 2-way fixed effects model we rely on numerical algorithms that exploit the particular structure of the model's normal equations. Although we find no evidence of endogenous sorting, we further limit potential biases from sorting by focusing on students with restricted course enrollment options due to low registration priorities, students not getting first section choices, and on courses with no within-term or within-year racial variation in instructors. We find that the performance gap in terms of class dropout rates, pass rates, and grade performance between white and underrepresented minority students falls by 20-50 percent when taught by an underrepresented minority instructor. We also find these interactions affect longer term outcomes such as subsequent course selection, retention, and degree completion. Potential mechanisms for these positive interactions are examined.
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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 | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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".