A man and his mic: Taking Chris Rock and Dave Chappelle to teacher’s college
Bibliographic record
Abstract
Taking seriously Donnell Rawling's advice that we need to interrogate our own "inner racism", this paper begins by examining work on anti-racism in North American education.Arguing that the narratives of diversity, equity, and social justice have paradoxically risen in prominence among educational researchers while their attempts to address equity issues in schools have simultaneously been resisted (Chase 2010), this paper advocates for the continued need to make discourses of race and racism explicit in educational settings (Lindo 2007(Lindo , 2010(Lindo , 2015;;Solomon & Levine-Rasky 2003;Bell 2009;Earick 2009).To this end, this paper presents and describes the work of "Race Comics" qua anti-racist educators and introduces the benefits of incorporating the comedic material of comedians like Chris Rock and Dave Chappelle in teacher education classrooms.Drawing on personal reflections of this pedagogical strategy in Canadian teacher education classrooms in Ontario (Canada) and Prince Edward Island (Canada), this paper teases out the ways in which these comedic texts in particular provided developing teachers with an opportunity to reflect upon their own normalised racial discourses, highlighting how these interfered with their ability to be the "perfect teacher".This paper concludes with a discussion of comedy's ability to help those devoted to developing socially just educational pedagogies to speak freely about their own normalised prejudices.In this way, "the man and his mic" facilitate explicit discussion of social inequities that, as critical race theorists like Derek Bell (1992; 2009) and Gloria Ladson-Billings (1995; 2009a; 2009b) have suggested, ensure that conversations about discourses of race and racism remain central in contemporary discussions about equity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| 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".