Analyzing Pluralized Moral Panics Using Morphological Framing: The Case of the Transgender Debate
Bibliographic record
Abstract
This article presents a new theory of pluralized moral panics that can help researchers make sense of the uniquely inflected conflicts that arise in our highly fragmented and mediatized world. Section one expounds and critiques both the classic theory of moral panics developed by Stanley Cohen, and the polarized theory presented by Iwona Zielińska and Barbara Pasamonik. Section two expounds and revises Michael Freeden’s morphological theory of ideologies to present a theory that is applicable to contemporary pluralized moral panics. Section three applies the new theory to the current transgender debate. This pluralized moral panic is shown to have five features that distinguish it from classic and polarized panics: (1) the fragmentation of disputant groups; (2) the proliferation of ideologies and interpretative bubbles; (3) continual reframing and counter-framing; (4) discontinuous moral panics; and (5) ambiguous responsibilities. These features are explored with reference to a range of disputants, including those within the New Christian Right, trans activist groups, Donald Trump and the MAGA movement, gender critical feminists, and pro-trans feminists. The argument concludes in section four by summarizing the argument and its significance.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.037 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".