From broken models to treatment selection: Active inference as a tool to guide clinical research and practice
Bibliographic record
Abstract
From Broken Models to Treatment Selection: Active Inference as a Tool to Guide Clinical Research and Practice Authors Lukas Kirchner Department of Psychology, Clinical Psychology and Psychotherapy, Philipps-University of Marburg, Marburg, Germany Anna-Lena Eckert Department of Psychology, Theoretical Cognitive Science, Philipps-University of Marburg, Marburg, Germany Max Berg Department of Psychology, Clinical Psychology and Psychotherapy, Philipps-University of Marburg, Marburg, Germany Abstract No abstract available. PDF HTML XML Article info Impact Citations How to Cite License Published at 30. June 2022 https://doi.org/10.32872/cpe.9697 Issue: Vol. 4 No. 2 (2022) Section: Editorial Share: Z Kirchner, L., Eckert, A.-L., & Berg, M. (2022). From Broken Models to Treatment Selection: Active Inference as a Tool to Guide Clinical Research and Practice. Clinical Psychology in Europe, 4(2), 1-5. https://doi.org/10.32872/cpe.9697 More Citation Formats ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver Download Citation Endnote/Zotero/Mendeley (RIS) BibTeX This work is licensed under a Creative Commons Attribution (CC BY) 4.0 International License. PlumX Dimensions Views: Total Abstract PDF HTML XML 370 190 122 53 5 Downloads: Download data is not yet available.
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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 | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".