Reticent on Race: Promoting Constructive Discussions about Race in a College Classroom
Bibliographic record
Abstract
This case study details the classroom dynamics of a Race and Ethnicity course and how to create a comfortable and engaging environment. To determine what students believe leads to a productive dialogue, two colleagues at a small liberal arts college in Maryland used in-depth interview data from ten students to identify four key pedagogical techniques. These strategies were the basis for teaching a group that includes students who are resistant to the existence and implications of white privilege. The data revealed that students want to feel like they are being educated, and not directed. Students’ desire to give input can be inhibited by instructors that have already decided the direction of the class, which deprives students of the chance to shape the conversation, feel engaged, confident, and empowered. Navigating and participating in a conversation about race is an important skill that is also an antidote to the dearth of productive dialogue in this arena. Students appreciate the ability to draw their own conclusions and to hone their critical thinking skills.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".