Intolerance of Ambiguity Mediates the Links Between Systems Thinking With Dichotomous Thinking and Attributional Complexity in a Canadian Sample
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Dichotomous thinking is often employed to break down and simplify wicked problems (e.g., climate change) arising from complex adaptive systems (CASs). However, these dilemmas display emergent properties that cannot be reduced to the individual systemic parts. In contrast, systems thinking is considered to be essential in understanding the behaviour of CASs. The capacity to work with the ambiguity of CASs and wicked problems has been posited to be an integral aspect of a systems mindset. If so, this may help to explain why systems thinkers are believed to be disinclined towards dichotomous thinking and why they prefer multicausal explanations for complex phenomena. Across 359 Canadian undergraduate participants, results showed that systems thinking negatively predicted dichotomous thinking and positively predicted attributional complexity. Intolerance of ambiguity partially mediated both of these associations. Therefore, systems thinkers may avoid using dichotomous modes of thought and prefer multicasual attributions because they are comfortable dealing with ambiguity.
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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 | 0.032 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it