Speculative science ("fairy tale science") in physics, cosmology, and economics
Bibliographic record
Abstract
The paper juxtaposes two recent books dealing with reality issues in a broad sense. The first of these, by Baggott (2013,) examines and criticizes the historically increasing trend to base scientific conclusions on mathematical hypotheses and logical consistency rather than on empirical evidence -- Bagott calls this trend “fairy tale science”. The second book, Tegmark (2014),i defends the opposite view – it considers Mathematics as identical to Reality and promotes increasing reliance of modern physics and cosmology on mathematical assumptions and logical consistencies rather than empirical evidence -- defending such controversial conclusions that we live in one of infinitely many parallel universes with numerous alter egos of each of us. But this is not a book review of Baggott (2013) and Tegmark (2014); its aim is to draw attention to the fact that social scientists are not the only scholars blamed for paying too much attention to model building and too little to empirical confirmation. This, ought to be of enormous interest to financial scholars; it may even be a consolation to some of them for emphasizing mathematical consistency rather than empirical confirmation. But our examples (from “speculative science” in finance) illustrate that such a trend caused staggering losses during the financial crisis of 1997-1998 (of Japan and Russia) and serious threats to the entire World Economic System during the crisis of 2007-2008
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".