Destigmatizing borderline personality disorder with social justice and intersectional cultural humility: How researchers can construct and deconstruct stigma
Bibliographic record
Abstract
Borderline personality disorder (BPD) is a serious psychiatric condition, especially stigmatized in women. Stigma is a social injustice, as it discredits and reduces the wholeness of a person to one of taint and discount. Psychological scientists play a uniquely powerful role in the stigmatization and destigmatization of BPD by constructing the meaning of BPD at each step of the research process. We discuss this powerful role and how to destigmatize BPD by incorporating an intersectionality framework that includes disability as a category of difference (as with gender, race, and sexuality). This framework centers the role of systems and structures in creating and maintaining stigma, while emphasizing the close interactions between interpersonal and structural stigma. This article illustrates researchers' power to assign meaning to BPD in research and highlights the importance of considering individuals as embedded in intersectional social categories, which are multidimensional and dynamic in nature. We propose that intersectional cultural humility, with its social justice aim and feminist origins, can guide BPD researchers to conduct nonstigmatizing and rigorous research on BPD. To inform clinical practice and advance social justice, we offer action steps for researchers to destigmatize BPD with intersectional cultural humility at multiple steps in the research process.
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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 | 0.100 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.014 | 0.104 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.005 | 0.013 |
| 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".