The Evolution of Animal Communication: Reliability and Deception in Signaling Systems. William A. Searcy and S. Nowicki
Bibliographic record
Abstract
Princeton, NJ: Princeton University Press, 2005. 288 pp. ISBN 0-691-07095-4. Are communicating animals straight-talkers or con-men? That is, when a gazelle stots, a songbird sings, or a mantis shrimp displays its weapons, are they sending a truthful signal to the receiver or trying to pull a fast one? The answer, like answers to most questions in biology, is that it depends. And it is how it depends that is the topic of Searcy and Nowicki's excellent treatise on animal communication. This book continues the eminent Monographs in Behaviour and Ecology series in fine form. Over the years the pendulum has swung back and forth between views of animals as honest advertisers or deceitful manipulators, and the debates have sometimes been heated and polemic. For example, the concept of information content in a signal has swung from defining communication to being regarded as unnecessary for the study of communication. Searcy and Nowicki do a great job of both reviewing the history of these debates and in many cases providing reconciliation. One perennially controversial issue is whether song dialect regions in songbirds serve as indicators of genetic differences and could potentially lead to local adaptations. The authors review these studies well, pointing out how some debates have been essentially beside the point (patterns of genetic change, timing of song learning), and how other problems are more critical for the local adaptation hypothesis (lack of evidence for local adaptation, limited female dispersal). In this and other topics the authors do exactly what a good synthesis should: review the current data and provide future direction.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".