Bibliographic record
Abstract
Abstract Wherever colonizing Europeans settled in the Americas from the 1490s through the turn of the 19th century, they brought their dogs with them. In New Spain, New France, New Netherland, and the Anglo-American colonies, European dogs participated actively in the colonizing project. War dogs helped in the conquest of Indigenous lands, guard dogs stood watch over enslaved laborers, and herding dogs protected the livestock species that colonizers introduced to the Americas and upon which colonial economies depended. At the same time, European colonizers often viewed wolves and Indigenous dogs as a threat. As conquest and epidemic disease devastated Indigenous human communities, their dogs either perished as well or were left to live feral in the countryside, where they were persecuted as threats to livestock. Wolves fared no better: private colonists and their governments hunted and trapped them, all while clearing the land upon which these predators depended to make room for agriculture and European livestock. By the end of the colonial period, both wolves and Indigenous-descended dogs had largely been eliminated from the portions of North America occupied by European settlers. Meanwhile, North America’s population of European-descended dogs continued to grow and expand their range, pushing into the continental interior as they accompanied settlers from Canada, Mexico, and the United States.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".