Bibliographic record
Abstract
PROBABLE DEDUCTION Peirce wrote extensively on deduction, induction, and hypothesis beginning with the Harvard Lectures of 1865 and Lowell Lectures of 1866. The ideas that he examined in those early discussions were reworked over nearly two decades until the comprehensive statement of his view contained in “A Theory of Probable Inference” of 1883 that was included in the Studies in Logic, by the Members of the Johns Hopkins University and is reprinted in W 4, 408-450. This remarkable paper developed a version of the Neyman-Pearson account of confidence interval estimation that incorporated the main elements of the rationale offered for its adoption in the early 1930s and presented it as an account of inductive inference. In his retrospective reflection on the question of induction in 1902 (CP 2.102), Peirce revealed satisfaction with the views on induction advanced in 1883 and this attitude is confirmed in other remarks from that period. However, Peirce did express dissatisfaction concerning his notion of “Hypothetic Inference.” Although Peirce called it Hypothetic Inference or Hypothesis from 1865 to 1883 and later, in 1902, Peirce replaced the term “Hypothesis” with “Abduction.” In what I said about “Hypothetic Inference” I was an explorer upon untrodden ground. I committed, though I half corrected, a slight positive error, which is easily set right without essentially altering my position. But my capital error was a negative one, in not perceiving that, according to my own principles, the reasoning with which I was there dealing could not be the reasoning by which we are led to adopt a hypothesis, although I all but stated as much. But I was too much taken up in considering syllogistic forms and the doctrine logical extension and comprehension, both of which I made more fundamental than they really are. As long as I held that opinion, my conceptions of Abduction necessarily confused two different kinds of reasoning. (CP 2.102)
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".