Bibliographic record
Abstract
This article explores whether student-centered discussion is an effective pedagogical method to create a sanctuary type of environment in racial justice courses. Although studies abound about race in university classrooms, none assess the impact of a cross-racial faculty team (Black and White) co-teaching a racial justice course that utilizes a student-centered discussion model. Thus, we build on critical race education scholarship’s insights to examine the impact of a student-centered discussion (SCD) model with a cross-racial faculty team co-teaching a racial justice course. Our data come from a two-year interpretive study that included direct observation and content analysis to examine whether student-centered learning is an effective pedagogical method for teaching racial justice courses. While we employed the Interactivity Foundation’s SCD model, our findings provide insights about general advantages and disadvantages of student-centered learning in racial justice courses. We conclude the SCD process both detracts from and contributes to the creation of a sanctuary classroom environment. This is complicated because what some White students found to be a sanctuary space was simultaneously a threatening space for Students of Color, particularly due to repeat racial microaggressions. Nevertheless, an SCD process can be beneficial if course structure purposefully decenters a White-centric curricular and pedagogical lens.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".