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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".