Stabilizing Constructs through Collaboration across Different Research Fields as a Way to Foster the Integrative Approach of the Research Domain Criteria (RDoC) Project
Bibliographic record
Abstract
More than 450 million people worldwide suffer from neuropsychiatric disorders and the numbers continue to rise (WHO, 2016). In 2010, aiming to solve the global mental health crisis and advance psychiatry toward a precision medicine approach, the US National Institute for Mental Health (NIMH) initiated the Research Domain Criteria (RDoC) Project (Cuthbert and Insel, 2013). Scientists at NIMH importantly recognize that understanding and explaining psychopathological phenomena requires input from different areas of science that investigate the role of different units of analysis (e.g., genes, cells, systems) in the production of organism-level behavioral functions. The RDoC matrix is put forward as a context for integrating results from these different sciences into a taxonomy of putatively valid constructs they purportedly share in common. It is intended to facilitate the development of “integrative psychobiological explanations” of those behavioral functions designated by the constructs (Cuthbert and Kozak, 2013, p. 931; See also Sanislow et al., 2010). Such explanations, by shedding light on the mechanisms of these functions, will enable investigators to pinpoint viable targets for therapeutic intervention in cases in which these functions are disrupted.
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How this classification was reachedexpand
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".