Bibliographic record
Abstract
Abstract Both terms in our title, “habit” and “generalization,” are ordinary language expressions that take a peculiar and abstract sense in Peirce’s thought. From various standpoints, the concepts denoted by these two terms prove to be fundamental for understanding Peirce’s ideas, and eventually for the further development of these ideas in the philosophy of science. My review suggests that Peirce’s thought moves toward a goal that he constantly suggests but never articulates explicitly. This unstated objective is no other than the goal of generalizing the very idea of generalization. This article demonstrates that once the notion of habit is generalized, its connotational range swells to cover such diverse instances as those of symbol, rule, propensity, and law of nature. Therefore, this expanded conception can be applied to unify previously separated strands of thought and scientific practice. These considerations lead me to speculate on the possibility of extending Peircean synechism toward a wider conception that could include the generalizing functions of ideas concerning symmetry (and symmetry breaking) and other kinds of invariance. A version of this paper was first presented at the conference “V Jornadas: Peirce en Argentina” at the Academia Nacional De Ciencias, De Buenos Aires, from August 23 to 24, 2012.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.024 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".