Bibliographic record
Abstract
This is an accepted article with a DOI pre-assigned that is not yet published.Fine-tuning arguments claim that the precise parameter values and initial conditions required for complexity and life are extraordinarily improbable, and demanding explanation. This paper examines whether such probability claims can be grounded in physics. The paper starts with a brief study of a historical case, Newton's design argument regarding the solar system, which suggests that assessments of "improbable coincidences" can result from overly narrow theoretical perspectives. The paper distinguishes between probabilities legitimately employed within physical theories and those invoked in fine-tuning arguments. While physics provides mathematical structures and dynamical justifications for probability assignments, fine-tuning arguments typically rely on unjustified applications of the principle of indifference over possibility spaces lacking unique measures. An appealing reformulation of the fine-tuning argument, due to Roberts, avoids this and other challenges, but then the argument then clearly depends on what the Designer regards as aimworthy rather than on physics. Closer examination of the aspects of physics routinely used in fine-tuning arguments exposes fundamental problems. "Naturalness" in particle physics conflates distinct concepts—autonomy of scales versus statistical typicality within theory space—where only the former is well-motivated. Cosmological measures lack the dynamical grounding that justifies using them to make probabilistic claims similar to equilibrium statistical mechanics. The paper concludes that fine-tuning arguments fail to establish their probabilistic claims, and as a result fail to provide a version of the argument from design that should compel those not already committed to demands for ultimate explanation. (Note that this is for a special issue edited by Eric Priest.)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| 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".