Epistemic disagreement in psychopathology research and practice: A procedural model
Bibliographic record
Abstract
Clinical psychology is characterized by persistent disagreement about fundamental aspects of the discipline ranging from what mental disorders are to what constitutes effective treatment. Attempts to address the problem of epistemic disagreement have been frequently based on establishing the correct answer by fiat without identifying and addressing the sources of the disagreement. We argue that this strategy has not worked very well and the result is frequently ongoing and intractable disagreement, with each side in an argument convinced they are correct. In this paper, we outline an epistemic disagreement procedural model intended to assist researchers and clinicians in the field of clinical psychology to identify, explore, and develop inquiry strategies that capitalize on situations where competing knowledge claims are made. The result is a flexible conceptual framework committed to a defensible epistemic pluralism, fallibilism, and contextualism, across the various research tasks constituting the field of clinical psychology.
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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 | 0.152 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.010 | 0.126 |
| Scholarly communication | 0.021 | 0.032 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".