Two Philosophies of ‘As If’: Vaihinger and Maimon on the Use of Fictions in Science and Metaphysics
Bibliographic record
Abstract
Thought about scientific models and modelling practices in the sciences has a long tradition. It has recently been argued that this practice of science also exists in metaphysics. In this paper, I show that this view has two significant historical forerunners: Hans Vaihinger and Salomon Maimon. Vaihinger provided what is today often seen as the starting point of the contemporary debate on scientific models as fictions. He argued that fictions can be equally useful in the sciences as in metaphysics. However, Vaihinger‘s position is problematic. Firstly, he mainly credits Kant for providing the first comprehensive account of the method of fictions in the sciences. I will argue that contrary to his claims, it was Maimon – whom Vaihinger only mentions in passing – who first adapted Kant‘s doctrine of ideas to serve the purposes of a fictionalist agenda. I will show that Maimon‘s account of scientific fictions already contains many of the features which Vaihinger claims to have discovered himself. Secondly, Vaihinger’s failure to distinguish between the use of fictions in the sciences and metaphysics creates a problem for some types of metaphysical fictions. I show that Maimon‘s account of metaphysical fictions identifies and avoids this problem.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".