Bibliographic record
Abstract
Cre-active problem-solving means the solving of problems of various degrees of importance and difficulty in due time and measure by catching and moulding the ‘kairos’ ‐ a nowadays again useful notion from Greek antiquity, which will be explained more thoroughly. Starting with an example of such cre-activity in everyday life, it will be shown that cre-active problem-solving presupposes mindfulness, rational sensitivity, stress resilience, psychological flexibility, and good judgment ‐ informed by situated cognition and critical thinking, by intellectual understanding as well as not immoderate emotions, by imagination of the unreal, but possible, and last but not least by a certain artfulness or cunning (in antiquity called ‘metis’) to profit from the uncertainty of critical situation, from the widening of horizons or the loopholes in the regular functioning of the world. Examples from the inventory of educational tools for developing the general and some more specific skills for cre-active problem-solving will be described in the second part of the paper.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".