Coping with stress through decisional control: Quantification of negotiating the environment
Bibliographic record
Abstract
Coping with stress through 'decisional control' - positioning oneself in a multifaceted stressing situation so as to minimize the likelihood of an untoward event - is modelled within a tree-structure scenario, whose architecture hierarchically nests elements of varying threat. Analytic and simulation platforms quantify the game-like interplay of cognitive demands and threat reduction. When elements of uncertainty enter the theoretical structure, specifically at more subordinate levels of the hierarchy, the mathematical expectation of threat is particularly exacerbated. As quantified in this model, the exercise of decisional control is demonstrably related to reduction in expected threat (the minimum correlation across comprehensive parameter settings being .55). Disclosure of otherwise intractable stress-coping subtleties, endowed by the quantitative translation of verbal premises, is underscored. Formalization of decisional stress control is seen to usher in linkages to augmenting formal developments from fields of cognitive science, preference and choice modelling, and nonlinear dynamical systems theory. Model-prescribed empirical consequences are stipulated.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".