Bibliographic record
Abstract
Abstract ‘Rensch’s Rule’ is known as a pattern of allometry in which the degree of male-biased sexual size dimorphism (SSD) increases with species body size. Over the last decades, a growing amount of Rensch’s Rule studies has advanced our understanding of SSD, its prevalence in nature, and the mechanisms underlying its evolution. However, Bernhard Rensch, when describing the pattern for the first time, considered the allometry of SSD only as a special case of a more general pattern in which dimorphism in any relative sexual difference increased with body size. In this perspective I revisit the history of Rensch’s Rule, starting with its popularization in recent decades, then diving into the original works by Rensch to rediscover his original observations, and finally discussing the implications of studying Rensch’s pattern beyond its applications to SSD. The strong bias towards body size in the study of Rensch’s Rule has proven valuable regarding our understanding of the evolution of SSD. Using empirical examples I propose, however, that expanding the study of the pattern to other traits might prove insightful for the general study of sexual dimorphism and phenotypic diversity.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".