Bibliographic record
Abstract
In Chapter 2, we first review the evolution of wolves into dogs and consider the economic concepts and models that help us understand how and why symbiosis between humans and dogs developed and the biological and socio-cultural forces that sustain symbiosis. These questions are the prelude to the economics of dogs in contemporary life. We use the Prisoner’s Dilemma game to illustrate how individuals seeking to maximize their own payoffs can lead to social inefficiency. In this game, the human “Clan” and the wolf “Pack” interact. However, interspecies interaction requires evolutionary game theory, where some wolves have a genetic trait that predisposes them to share with humans. The Hawk-Dove game features equilibria that can result in diverse genetic strategies. Experiments with captive foxes that have little associative behavior with humans show that greater cooperation with humans can be induced quickly through selective breeding. We then build on these concepts and games to introduce the economics of dog co-production with humans. We show how this generates incentives for humans to create distinct types of dogs, or breeds.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.057 | 0.028 |
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".