The Shortcomings of Extra-Empirical Justifications for String Theory
Bibliographic record
Abstract
The main goal of the thesis is to level counterarguments against Richard Dawid’s extra-empirical arguments for the scientificity of string theory. I will use the luminiferous aether as a parallel case study to show that if we were to accept Dawid’s extra-empirical arguments, an equally compelling case could have been made for the scientificity of Henri Poincaré’s aether theory—which has since been refuted. I also suggest that the lack of empirical corroboration of supersymmetry at the Large Hadron Collider undermines the trustworthiness of his meta-inductive argument in particular and that Dawid may be guilty in this argument of committing the base rate fallacy as identified in the Bayesian probability theory on which he relies. Furthermore, I argue that since there is a substantive difference between how Dawid’s extra-empirical arguments are used in the canonical scientific method and how he proposes to use them in support of string theory’s scientificity, the former cannot justify the latter. Lastly, I object to taking string theory’s final theory claims seriously since certainty of string theory’s scientific validity is their truth condition, and empirically or extra-empirically, this is unobtainable.
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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 | 0.036 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".