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Record W1922243494 · doi:10.7592/ejhr2015.3.4.lindo

A man and his mic: Taking Chris Rock and Dave Chappelle to teacher’s college

2015· article· en· W1922243494 on OpenAlexaffabout
Laura Mae Lindo

Bibliographic record

VenueEuropean Journal of Humour Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsMathematics educationPsychology

Abstract

fetched live from OpenAlex

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 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.215
GPT teacher head0.476
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2015
Admission routes2
Has abstractyes

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