Bibliographic record
Abstract
How should we evaluate Darwin and Wallace's arguments for common ancestry over separate ancestry? Elliott Sober defends a likelihood reconstruction of Darwin's reasoning that he dubs modus Darwin: similarity, therefore common ancestry. One assumption of Sober's approach is that separate ancestors have traits that are probabilistically independent. I motivate an objection to this assumption by appeal to 19th century naturalist alternatives such as those of Geoffroy and Owen. On Geoffroy and Owen's separate ancestry models, the ancestors can have traits that are probabilistically dependent. I then prove a generalization of Sober's approach that allows for similarity matching among traits to favour common ancestry over separate ancestry even when the traits of the separate ancestors are probabilistically dependent. I consider Helgeson's recent criticisms of Sober's approach and his alternative interpretation of Darwin's reasoning: more similar, hence, more recent common ancestry. I defend Sober's approach against Helgeson's objections.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".