Editorial: Challenges of Interdisciplinary Research in the Field of Critical (Sex/Gender) Neuroscience
Bibliographic record
Abstract
Challenges of Interdisciplinary Research in the Field of Critical (Sex/Gender) NeuroscienceThere is currently widespread agreement among scholars that neuroscientific investigations that purport to delineate sex-and gender-related structural and functional brain differences urgently require conceptual critique, methodological nuance and thorough reflexivity about the research questions, operationalization, interpretations and implications shaping this scholarship (Fausto-Sterling, 2000;Fine, 2010;Jordan Young, 2010;Roy, 2012).In response to this need, the seven articles in this collection demonstrate new avenues in critical interdisciplinary scholarship in the field of sex/gender and neuroscience research, including approaches that draw on feminist science studies and critical neuroscience.Since the first publications that show how social and cultural values pervade the formulation of biological research on sex and gender, enormous developments have also occurred in the neurosciences, with increased evidence from functional neuroimaging and epigenetics pointing to the context-sensitivity and contingencies of brain development and function.This underscores the imperative for researchers to consider carefully their treatment of difference and of their conceptions of complexity and diversity.It is clear that we need to work out how to collaborate across epistemic boundaries, how to refine and draw on social theory to make sense of brain findings and how together this can inform interpretation of experimental data, data that bear relevance to the real world.This Frontiers research topic builds on a key insight by critical feminist scholars: to arrive at a critical and more socially just production of knowledge about human behaviour it is important to go beyond the split between second order and first order observations, i.e., between critical sociological observations about neuroscientific practice and experimental investigations of the brain.Investigating and responding (to) this goal, the studies in this collection show how, in varying ways, scientific disciplines newly interact and may also clash in the formation of new conceptualizations of the relation between gender, sex and the material brain.The collection thus contributes to a better understanding of inter-or multi-disciplinary relations necessary to advance a study of the brain and human behaviour that is crucially informed by a feminist agenda.Moreover, improving our knowledge of (inter-)disciplinary epistemic dynamics by means of the specific case studies in this collection also offers background to an ongoing discussion about how to realize intersectional research.The past 2 decades saw the emergence of a number of sub-(inter-)disciplinary labels and scholarly networks such as "critical neuroscience" (Choudhury and Jan, 2012, Kirmayer and Crafa, 2014), "neurofeminism" (Roy, 2008;Bluhm et al., 2012;Schmitz and Höppner, 2014), "neurogenderings" (Dussauge and Kaiser, 2012;Fitsch, 2012) and "neurocultures" (Schmitz and Höppner, 2014;Vidal and Ortega, 2018), as examples of the heterogeneous bodies of knowledge and gatherings of scholarship (sometimes converging, sometimes conflicting) that aim to analyse fundamental
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 | 0.016 | 0.060 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.039 | 0.037 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".