Bibliographic record
Abstract
Introduction When John Maynard Smith (1966) wrote on sympatric speciation more than 35 years ago, he acknowledged that the argument “whether speciation can occur in a sexually reproducing species without effective geographical isolation” was an old problem and voiced his opinion that the “present distribution of species is equally consistent either with the sympatric or the allopatric theory.” Yet, from the heyday of the Modern Synthesis until relatively recently, the importance of sympatric speciation has been downplayed, and the corresponding hypotheses remained obscure well beyond Maynard Smith's seminal study. Looking back from today's perspective, it is astounding that, for such a long period, the research community at large essentially turned a blind eye to sympatric speciation. Given the widely acknowledged difficulties involved in inferring past process from present pattern, one can only feel uneasy about a logic that claims to find evidence for the prevalence of allopatric speciation in the present-day distribution of species. To a large extent it seems to have been the scientific community's perception of the theory of sympatric speciation that has brought about a profound skepticism toward the broader empirical relevance of this speciation mode. Scientific attempts to overcome this skepticism have come and gone in waves. In the 1960s, luminaries of North American evolutionary biology pulled no punches when assessing the merit of such attempts.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".