Bibliographic record
Abstract
In behavioral psychology, function typically refers to the appetitive consequence, or reinforcer, that maintains a given behavior and causes it to occur more frequently. The primary method to identify the function of maladaptive or problematic behavior is through a method known as functional analysis . The present study was a comprehensive review of functional analysis used with dogs, to identify common reinforcers of various problem behaviors observed in dogs. The functional analysis method was effective at identifying the function of dog behavior problems in 27 of 28 cases, indicating that functional analyses are an efficacious method to better understand the reinforcer(s) for behavioral problems observed in dogs. Common reinforcers for different topographies as well as correlations between dog breeds, behaviors, and reinforcers are discussed. In addition to the empirical review, this study discusses the advantages and disadvantages of functional analysis methods as well as the current state of the literature as it relates to improving animal welfare broadly, and interventions for behavior problems observed in dogs specifically. • Functional analysis identified dog behavior function in 27 of 28 reviewed cases • Jumping up was the most common problem behavior across dog breeds • Attention and tangibles were top reinforcers maintaining problem behaviors • Functional analysis offers more accuracy than surveys for dog behavior problems • Strong links found between dog breed, behavior type, and behavioral function
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".