Functional Constructivism Approach to Multilevel Nature of Bio-Behavioral Diversity
Bibliographic record
Abstract
Attempts to revise the existing classifications of psychiatric disorders (DSM and ICD) continue and highlight a crucial need for the identification of biomarkers underlying symptoms of psychopathology. The present review highlights the benefits of using a Functional Constructivism approach in the analysis of the functionality of the main neurotransmitters. This approach explores the idea that behavior is neither reactive nor pro-active, but constructive and generative, being a transient selection of multiple degrees of freedom in perception and actions. This review briefly describes main consensus points in neuroscience related to the functionality of eight neurochemical ensembles, summarized as a part of the neurochemical model Functional Ensemble of Temperament (FET). None of the FET components is represented by a single neurotransmitter; all neurochemical teams have specific functionality in selection of behavioral degrees of freedom and stages of action construction. The review demonstrates the possibility of unifying taxonomies of temperament and classifications of psychiatric disorders and presenting these taxonomies formally and systematically. The paper also highlights the multi-level nature of regulation of consistent bio-behavioral individual differences, in line with the concepts of diagonal evolution (proposed earlier) and Specialized Extended Phenotype.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".