Bibliographic record
Abstract
INTRODUCTION In this chapter, I show how Peirce's model of mind is grounded in his semeiotic, or general doctrine of signs, a grounding made possible by the logical priority, in Peirce's thought, of the concept of sign over the concept of mind. I then compare this model of mind with some more recent doctrines and theories, and conclude with some comments on Peirce's relevance for cognitive science, including both artificial intelligence and human-computer interaction. PEIRCE’S DOCTRINE OF SIGNS Peirce’s doctrine of thought signs was first introduced in his justly famous 1868 articles in The Journal of Speculative Philosophy and later developed in greater detail from 1895 until Peirce’s death in 1914. In his 1868 papers Peirce specifically targeted Descartes and Cartesianism, and argued that we have no ability to think without signs. This argument presupposes a prior argument that all self-knowledge can be accounted for as inferences from external facts and that there is thus no reason to posit any power of introspection (CP 5.247–9). We need, therefore, to look to external facts for evidence of our own thoughts, and it is then a near-tautology to conclude that the only thoughts so evidenced are in the form of signs: “If we seek the light of external facts, the only cases of thought which we can find are of thought in signs” (CP 5.251).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".