Diminishing defensiveness in anti-racist discourse: Common pushbacks to online anti-racism content and suggestions for strategic maneuvers
Bibliographic record
Abstract
When students are confronted with the knowledge that racism exists and detrimentally impacts the lives of others, there can be a sharp pushback experienced by anti-racist educators. However, not providing anti-racist education can lead to radicalization which causes further harm. However, by analyzing common “pushback” responses, educators can enter these spaces with knowledge to help strategize ways to guide people away from defensiveness, fear, and other strategies of resistance. Thus, our article aims to address the question: “What are common push-back maneuvers encountered in response to anti-racist education on social media?” This article draws on social media comments listed in response to four anti-racism educational films developed by the authors, which encompass both the racism expressed in response, and the ways that some commenters dealt with this pushback. The films were viewed nearly half a million times, and inspired many comments, both positive and negative. We analyzed these comments and noted a strong theme of push-back to anti-racism work, which we broke into specific categories. This article details the categories of this push-back, along with ways to move against its flow. We would like to remind readers that the content in this article involves direct quotations from public comments which include racism and hate and can be difficult or triggering to read.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".