Suspending Stigma: Bounded Destigmatization in Liminal Contexts
Bibliographic record
Abstract
Stigma is a persistent form of negative social evaluation that devalues individuals and groups, reinforcing normative societal hierarchies. While prior research accepts that stigma is enduring, it also understands that stigma is socially constructed and focused on uncovering the dynamics of stigmatization and destigmatization. However, these accounts often portray a gradual, linear process in which a category becomes either stigmatized or destigmatized. In contrast, our paper introduces the concept of bounded destigmatization. We argue that even amidst persistent stigma there are situations in which the stigma is lifted, even if only temporarily. We draw on the scholarship on liminality to theorize how spatiotemporal contexts promote the suspension of stigma. We contend that stigmatized actors actively construct liminal spaces – contexts where normative hierarchies are disrupted, symbolic boundaries redefined, and stigmatized identities reimagined – where the negative judgment of stigma does not apply. These contexts serve as temporary shelters that foster solidarity, create alternative value systems, and allow stigmatized individuals to engage in authentic interactions. We thus contribute to stigma research by emphasizing how stigmatized groups actively mobilize liminality to construct safe spaces where they can embrace their practices and identities without the stigmatizing gaze. We uncover the boundary work they engage into to construct such spaces and suggest avenues for future research on sustaining spaces of bounded destigmatization and fostering broader social transformation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.039 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| 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".