Bibliographic record
Abstract
Affective instability is a psychophysiological symptom observed in some psychopathologies. It is a complex construct that encompasses (1) primary emotions, or affects, and secondary emotions, with each category having its own characteristics, amplitude, and duration, (2) rapid shifting from neutral or valenced affect to intense affect, and (3) dysfunctional modulation of emotions. Affective instability is often confused with mood lability, as in bipolar disorders, as well as with other terms. To clarify the concept, we searched databases for the term affective instability and read related articles on the topic. In this article we situate the term within the current affective nomenclature and human emotional experience, explore its psychophysiological features, and place it within the context of psychopathology. We explain why the term can potentially be confused with mood pathology and then define affective instability as an inherited temperamental trait modulated by developmental experience.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".