Time to Rethink the Default Settings in Neuroscience: Hormonal Transition Periods as Natural Experiments and Why Sex Matters.
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
Diversity drives scientific discovery. Yet, many basic and clinical neuroscience studies fail to include equal numbers of females in their samples, and even fewer present sex-specific analysis of their data. We propose the following strategies to overcome this bias: (1) increase numbers of female study participants, (2) consider sex as a primary variable, and (3) when justified, study all-female samples to provide a more indepth understanding of female-specific experiences such as the menstrual cycle as well as sex-specific risk trajectories and pathologies. In our research program, we study the influence of sex and sex hormones on brain states in health and disease. We strive to explain the mechanisms underlying the unique vulnerability of women to depression and dementia. Our ultimate goal is to improve brain health for both sexes. By applying scientific expertise in neuropharmacology, quantitative neurochemical imaging and sex differences to traditional research questions in the cognitive sciences, we provide novel perspectives on the diversity of human cognition and brain plasticity. Knowledge gaps and missing data are often the primary driving force behind the development of scientific breakthroughs. The curiosity and ur-
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 it