Bibliographic record
Abstract
C. S. Peirce had a keen interest in the most mathematical economics of his era. We know that Peirce read and wrote about the mathematical economics of Cournot and Jevons and at least mentions the names of Ricardo, Marshall, and Walras. Peirce also provided a mathematical, optimizing model of the insurance firm as his most elaborate example of pragmatism in the Harvard Lectures of 1903. What is significant is that Peirce chose economic examples to illustrate what is really a semiotic and mathematical conception of pragmatism. Diagrams and semiotics play a central role in Peirce’s philosophy of mathematics. Just a few years ago, Carsten Herrmann-Pillath authored a long treatise on evolutionary economics with Peirce’s semiotics as a central aspect of that work. Additionally, Herrmann-Pillath makes significant use of diagrams and equations from various scientific disciplines. Diagrams are a central feature of Herrmann-Pillath’s treatise giving it something of a Peircean, qualitative mathematical character. These similarities and differences between Peirce and Herrmann-Pillath on semiotics, economics, mathematics, and evolutionary processes are quite novel and thus of intrinsic interest.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".