Thoughts about intersectionality and risk. Interviews with key scholars
Bibliographic record
Abstract
Since the early twenty first century, feminist and intersectional approaches to risk research have gained momentum, initially emerging from studies on HIV risks within health studies. Over the past decade, these approaches have expanded to other fields. As editors of this special issue, Katarina Giritli Nygren and Anna Olofsson introduce a reflection piece anchoring the issue. The reflection piece includes insights from seven influential scholars in the intersectionality, equality, and risk fields: Lisa Bowleg, Dean Curran, Kelly Hannah Moffat, Claudia Mitchell, Lori Peek, Ignacio Rubio C., and Jens O. Zinn. Each scholar offers personal reflections on the development of intersectional analyses in risk research, highlighting key areas for future research. Three themes emerged: challenging risk as a neutral concept, addressing the complexity of risks in everyday life, and navigating between social structures and identity struggles. Contributors argue for contextualising risk within broader societal structures, embracing complexity, and understanding the intertwined nature of inequalities. Some, but not all, also advocate for intersectionality as a critical concept for studies of systemic change and equality. Overall, the reflections underscore the importance of centring intersectionality in understanding the dimensions of inequality and risk. The piece concludes by calling for further conversations and reflections to deepen our understanding of risk mobilisations and their links to inequality, both locally and globally. Such conversations can challenge assumptions and revitalize risk research, envisioning alternative worlds that prioritize equality.
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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 | 0.033 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.039 | 0.052 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.010 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 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".