Thoughts about intersectionality and risk. Interviews with key scholars
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.096 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it