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 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 | 0.368 | 0.689 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.012 | 0.035 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".