“The fuss is only just beginning”: Jordan Peterson and the mainstreaming of anti-gender ideology
Bibliographic record
Abstract
In this paper, we show that best-selling author Jordan Peterson has become an influential promoter of anti-gender ideology, particularly in the context of rising radical right-wing populism. We demonstrate that Peterson’s promotion of this ideology has increased over time, beginning as an apparent defense of free speech in 2016 and escalating into a direct attack on trans people and gender-affirming care in 2022. Though his views on gender have sparked intense debate, his alignment with anti-gender movements has yet to be carefully considered. With few exceptions, he has avoided being associated with them and is rarely seen as one of their most prominent proponents. Instead, Peterson is often assumed to have challenged gender-neutral pronouns and those who use them singlehandedly, as a one-man culture warrior. We show that this assumption is inaccurate, and that Peterson’s discursive strategies align with well-established anti-gender movements. Ultimately, we show that Peterson is a prime example of how radical right-wing views of gender are generated not only on the fringes of society but also in the mainstream, demonstrating how discursive strategies like his can move from marginal positions on the political spectrum to positions of greater prominence, reshaping the boundaries of what is considered both credible and acceptable in the public sphere.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".