Bibliographic record
Abstract
From the discovery of the gonadal neuroendocrine axis to projects mapping gender/sex diferences in brain and behavior, research in neuroscience has laid the foundation in biosciences for the investigation of sex and, to a lesser degree, gender. Given its role in sex and gender research, neuroscience has also been a central site of critical engagement by feminist science scholars, giving rise to neurofeminism, a subfeld where neuroscience and feminist perspectives on science intersect. To date, neurofeminism has produced critiques, research frameworks, methodologies, epistemologies, and neuroscientifc knowledge that coalesce to advance complex and emancipatory understandings of brain, body, and mind. This chapter aims to demonstrate the instrumental role of neurofeminist research and perspectives in producing alternative operationalizations of sex and gender, particularly with respect to their interrelation. First, a critical overview of dominant and emerging models for investigating sex and gender in neuroscience is provided to highlight benefts of approaching sex and gender as biosocially entangled. Second, the neurofeminist perspective on sex and gender entanglement is further characterized through a series of examples from human and nonhuman animal research. Consideration is then given to potential challenges associated with the neurofeminist approach to entanglement. Finally, the generative potential of neurofeminist science scholarship to improve the science of sex and gender is demonstrated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".