Exploring the boundaries and ontology of Psychiatric Disorders (PDs) using the Homeostatic Property Cluster (HPC) model
Bibliographic record
Abstract
In this article we show that, even though the classification and diagnosis of Psychiatric Disorders (PDs) are performed according to essentialist terms, the psychiatric diagnoses currently employed, (i.e., clinical psychiatry) do not actually meet these criteria. Diagnosis is performed operationally. In this paper, we suggest a change of perspective. We reject essentialism relating to PDs and argue for the Homeostatic Property Cluster (HPC) model, which allows a greater insight into the ontology of PDs than the operational perspective. More specifically, we argue that the HPC model allows for a synthesis of continuous and discrete methods of understanding the boundaries between PDs. Finally, we specify in a more general manner, the kind of ontology we deal with when adopting the HPC model, arguing that this model can be viewed as a mirror device, reflecting the ontological features of PDs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".