Condemning Intersectionality: Online Conservative News Media and Intersectional Panic
Bibliographic record
Abstract
Research has increasingly drawn attention to the role of online conservative news media in propagating disinformation and reinforcing social inequalities. Scholarship, however, has yet to explore how these media represent intersectionality. Using a grounded theory approach, I examined how 427 online conservative news reports, from nine widely searched websites in the U.S., portrayed intersectionality. The authors of the reports employed a complex set of discourses to condemn intersectionality, constructing it as limited, hierarchical, and divisive, while also conveying panic over its ability to bring individuals on the Left into coalitions. I thus develop the concept intersectional panic to account for how these media responded to intersectionality with a considerable amount of fear or anxiety. Findings reveal that intersectional panic overlaps with, yet also operates differently from, other forms of panic, such as racist, sexist, or anti-LGBTQ fears, because the former involves anxiety over multiply-marginalized individuals advancing in U.S. society. I further reveal that these conservative news media sometimes used intersectional discourses to condemn intersectionality. Building on Patricia Hill Collins’s (2019) understanding of intersectionality as a critical tool for social justice, I argue that emphasizing intersectionality’s expansive and beneficial capacities would help challenge such panic.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".