Bibliographic record
Abstract
In Chapter 3, we analyze the demand side of the market for dogs. We first consider some implications of the tension between treating dogs as property and their commonly perceived status as family members. We report on the increasing prevalence of dogs in U.S. households and review the survey evidence that shows that a large majority of households now consider dogs to be family members. We describe some of the factors and reasons that affect the demand for dogs in general, and the differences across socio-demographic groups. We consider whether pets are (economic) substitutes or complements for children. We address particularly economic explanations for potential inefficiencies (market failures) in the demand for dogs. We then consider demand for specific types or breeds of pet dogs, which can be explained partly by the theory of fads. Finally, we consider dog ownership in two circumstances: first, dog ownership by the homeless; second, the (perhaps temporary) increase in dog ownership during the COVID pandemic. We use microeconomic analysis of markets to help understand the surge in dog ownership induced by COVID and predict its long-term impacts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.028 |
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".