Bibliographic record
Abstract
In Chapter 7, we expand on the working roles of dogs and classify current canine occupations. We introduce the theory of comparative advantage and note its important role in evolutionary science. We classify canine occupations in terms of two dimensions: the type of dog advantage (comparative, absolute, or unique) and whether the occupation requires a higher or lower level of training. These occupations include service (guide, hearing, disabled, and psychiatric assistance), emotional support, therapy, hunting, herding, racing, search & rescue, substance detection, police work, diabetic alerting, cancer detection, and seizure alerting. We explain the trade-offs between selection and training across occupations, both in terms of breeds and juvenile dogs within breeds. We examine two studies that employ cost-benefit analysis. First, we present an analysis of the social benefits of guide dogs. Second, we discuss the controversy surrounding the treatment of emotional support animals in air travel and the cost-benefit analysis the Department of Transportation used to support its rule that allowed airlines to treat emotional support animals as pets rather than as service animals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.036 |
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".