Evaluating Rudner’s Quinean Critique of Carnap
Bibliographic record
Abstract
In “The Scientist Qua Scientist Makes Value Judgments,” Richard Rudner famously argues that inductive risk considerations demonstrate the value-ladenness of science. This argument from inductive risk has received much attention from philosophers of science who use it in critiquing the scientific, value-free ideal. What is less well-known is that later in the same paper Rudner uses a different sort of argument for the value-ladenness of science, one engaging the debate between W. V. Quine and Rudolf Carnap on the ontological implications of adopting language systems. With the introduction of linguistic frameworks, Quine, the nominalist, admits only objects, and not types. Comparatively, Carnap admits both objects and types, in the context of linguistic expressions subject to truth or falsity. For both Quine and Carnap, the introduction of linguistic frameworks is practically determined, their differences on matters of ontology running tangentially to this issue. I conclude that Carnap and Quine are committed to value-laden science, as Rudner suggests, though not in the rich sense meant by Rudner (and Otto Neurath). For both Carnap and Quine, linguistic framework decisions are only guided by a narrow set of instrumental, practical values—simplicity, expedience, fruitfulness and efficiency.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".