Function predicts how people treat their dogs in a global sample
Bibliographic record
Abstract
Dogs have an extraordinary relationship with humans. We understand, communicate, and cooperate remarkably with our dogs. But almost all we know about dog-human bonds, dog behaviour, and dog cognition is limited to Western, Educated, Industrialized, Rich, Democratic (WEIRD) societies. WEIRD dogs are kept for a variety of functions, and these can influence their relationship with their owner, as well as their behaviour and performance in problem-solving tasks. But are such associations representative worldwide? Here we address this by collecting data on the function and perception of dogs in 124 globally distributed societies using the eHRAF cross-cultural database. We hypothesize that keeping dogs for multiple purposes and/or employing dogs for highly cooperative or high investment functions (e.g., herding, guarding of herds, hunting) will lead to closer dog-human bonds: increased primary caregiving (or positive care), decreased negative treatment, and attributing personhood to dogs. Our results show that indeed, the number of functions associates positively with close dog-human interactions. Further, we find increased odds of positive care in societies that use herding dogs (an effect not replicated for hunting), and increased odds of dog personhood in cultures that keep dogs for hunting. Unexpectedly, we see a substantial decrease of dog negative treatment in societies that use watchdogs. Overall, our study shows the mechanistic link between function and the characteristics of dog-human bonds in a global sample. These results are a first step towards challenging the notion that all dogs are the same, and open questions about how function and associated cultural correlates could fuel departures from the 'typical' behaviour and social-cognitive skills we commonly associate with our canine friends.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".