On the Problem of Breathing, Eating, & Drinking Poison: An introduction to problem solving, nobility of purpose under adverse circumstances, and the search for truth with Sir Karl Popper on Prince Edward Island
Bibliographic record
Abstract
This paper introduces Karl Popper's approach to problem solving in the social sciences. These methods fundamentally represent the scientific method of the natural sciences. Popper's problem solving technique is outlined in six steps, including an introductory treatment of his solution to Hume's Problem of Induction. These six steps are then applied in the form of a test and logical deduction of our illustrative theory: Cancer rates on Prince Edward Island have dramatically increased as a result of an extraordinary increase (900% in the past decade) in potato production, and a corollary increase of secondary agricultural inputs, namely an increase of chlorothalonil (trade name: Bravo) applications in less than ten years. We conclude our theory is true and, in order to complete our demonstration of Popper's methods, open this theory to criticism and refutations. APPENDIX A offers a brief review of relevant literature on the philosophy of science, and APPENDIX B offers readers a brief introduction to the fundamentals of relevant island-based methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".