Bibliographic record
Abstract
Logic may well be the single most important key to understanding Peirce's thought and influence. It was his deductive logic that brought him an international reputation in his lifetime and led to conspicuous references to his work by figures such as Peano, Schröoder, Russell, Venn, Jevons, and Clifford. Peirce's highest, and in fact only, academic position was as lecturer in logic at Johns Hopkins University. He himself said on numerous occasions - when he wasn't emphasizing his role as a working scientist with the Coast Survey, that is - that he was mainly a logician. He called his existential graphs his chef d'oeuvre . Logic, especially the logic of relations, played a central role in the development of his philosophy. His three Categories were based on, and shown to be fundamental by, the logic of relations. The logic of relations is central to his analysis of the fundamental triadic notion of his semeiotics, “ __ signifies __ to __ .” He saw his theory of scientific method as just logic, broadly construed. And of pragmatism itself, he often repeated that it was nothing more than the ideal fixation of belief, and this was the very goal of logic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".