Bibliographic record
Abstract
In this epilogue, we reflect on the prospects for advancing interdisciplinarity in the sciences of culture, mind, and brain. Neuroscience is increasingly applied to address questions of central concern to the social sciences. Social sciences, in turn, can contribute to neuroscience research in a variety of ways, including: (1) the study of social factors that influence the brain across the lifespan; (2) the context-sensitive translation of neuroscience research into applications in clinical and other social settings; (3) critical social analyses of cultural, conceptual, and institutional framing and constraints on neuroscience research, knowledge production, and applications; and (4) integration of each of these approaches in an ecosocial view of the brain in its social-cultural niche. Obstacles to interdisciplinarity stem from institutional structures, methodological strategies, epistemic commitments, and divergent ontologies. We describe strategies to surmount these obstacles, including: (1) institutionally, creating spaces for collaborative work, supporting interdisciplinary career tracks, and ensuring sustained funding; (2) conceptually, borrowing models and metaphors across disciplines, establishing boundary objects of common interest, using system diagrams to locate diverse levels and processes in the same model; and (3) methodologically, establishing convergent validity through mixed and hybrid methods, and creating shared databases and pipelines to facilitate integration of multiple perspectives.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.298 | 0.121 |
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".