ManyDogs Project: A Big Team Science Approach to Investigating Canine Behavior and Cognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it