Bibliographic record
Abstract
The objective of the family of models 'Singing to Neighbours' is to explore the mechanisms that may be responsible for the strong correlation between the song types and habitat types observed in populations of South American Rufous-collared Sparrows, Zonotrichia capensis. Formal models of this type could be used to address outstanding objections to Cultural Selection Theory, according to which Darwinian processes of blind variation, heredity, and selective retention operate directly on cultural objects. In particular, 'Singing to Neighbours' can offer a better understanding of the crucial relationship between culture and environmental selection pressures. What is vital for Darwinian evolution is that there exists a directional selection pressure – as contrasted with a system’s inherent rate of change by the introduction of selectively neutral variations – and that this pressure has the effect of weeding out variations in the population that are less successful at reproducing under these conditions. The acoustic adaptation of the songs of the Rufous-collared Sparrow shows potential to serve as a such case study since it can clarify the role of environmental interaction in cultural evolution. Importantly, this system is not vacuously memetic, and there is potential to reveal the details of the selection mechanism through further investigations. One key part of this process can be played by spatial Game Theoretic models such as 'Singing to Neighbors,' which may have the resources to clarify the selective mechanisms underlying these systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".