Projectibility and Science: Epistemic Challenges
Bibliographic record
Abstract
Appealing to science is a popular suggestion for separating projectible predicates. According to this suggestion, we can expect science, eventually, to separate such predicates for us, rendering it unnecessary to make further attempts to explicate the criteria for projectibility. In this essay, I address three theoretical challenges to this suggestion. The first stems from the inductive character of science, which casts doubts on its efficacy in separating projectible predicates, since induction itself requires this separation. The second is the inferential externalism implied by this suggestion, whereas the problems of induction have bite only if inferential internalism is presumed. The third challenge appears when a strong relationship between projectibility and kindhood, on the one hand, and kindhood and similarity, on the other, is posited, such that a more projectible predicate is believed to be a predicate which tracks better similarities in nature. Now the question appears: whether and how science enables us to track better similarities? I distinguish two conceptions of similarity, one intuitive, the other theoretical, and I argue that the theoretical one is to be preferred, showing that how scientific practice involves shifting from an intuitive idea of similarity to a theoretical one. Through answering these three challenges, I attempt to support the appeal to natural science as far as Goodman’s problem of induction is concerned.
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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 | 0.029 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.079 |
| Scholarly communication | 0.015 | 0.040 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 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".