ManyDogs Project: A Big Team Science Approach to Investigating Canine Behavior and Cognition
Bibliographic record
Abstract
Dogs have a special place in human history as the first domesticated species and play important roles in many cultures around the world. However, their role in scientific studies has been relatively recent. With a few notable exceptions (e.g., Darwin, Pavlov, Scott, and Fuller), domestic dogs were not commonly the subject of rigorous scientific investigation of behavior until the late 1990s. Although the number of canine science studies has increased dramatically over the last 20 years, most research groups are limited in the inferences they can draw because of the relatively small sample sizes used, along with the exceptional diversity observed in dogs (e.g., breed, geographic location, experience). To this end, we introduce the ManyDogs Project, an international consortium of researchers interested in taking a big team science approach to understanding canine behavioral science. We begin by discussing why studying dogs provides valuable insights into behavior and cognition, evolutionary processes, human health, and applications for animal welfare. We then highlight other big team science projects that have previously been conducted in canine science and emphasize the benefits of our approach. Finally, we introduce the ManyDogs Project and our mission: (a) replicating important findings, (b) investigating moderators that need a large sample size such as breed differences, (c) reaching methodological consensus, (d) investigating cross-cultural differences, and (e) setting a standard for replication studies in general. In doing so, we hope to address previous limitations in individual lab studies and previous big team science frameworks to deepen our understanding of canine behavior and cognition.
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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 | 0.054 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".