From Foe to Friend: Exploring State-Led Destigmatization
Bibliographic record
Abstract
We seek to understand the distinctive process of state-led category destigmatization, extending an emergent stream of research on category destigmatization that has so far focused on the efforts of stigmatized members of categories. When a category conflicts with a prevailing ideology, internal actors may face such substantial barriers to destigmatization that the state—often motivated by the pragmatic benefits of that category’s success—must take a proactive role in the effort. Focusing on an extreme case, we explore the revival of the private business category in China through a longitudinal case study. We develop a grounded process model that highlights the interplay among the state, category members, and the public as a framework for understanding this type of destigmatization process. Our model also addresses dynamics that can emerge within the state when political power is divided between category proponents and opponents with competing ideological stances. Our study highlights the need for the state to balance destigmatization efforts with maintaining legitimacy, prompting iterative strategic adjustments based on local feedback, evolving public opinion, and intrastate competition between political factions. Our findings show that such adjustments may be needed even in authoritarian states, which are typically more coercive. In addition, we find that states can effectively use backstage strategies (e.g., regulatory leniency) and frontstage strategies (e.g., legislative change) in complementary ways to advance destigmatization while safeguarding the state’s legitimacy. Finally, we show that starting a category destigmatization effort by emphasizing the category’s pragmatic values (prior to advocating for moral reevaluation of the category) can mitigate ideological conflict and increase chances of successful destigmatization.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".