Lessons We Should Learn from Our Unique Relationship with Dogs: An Ethological Approach
Bibliographic record
Abstract
This chapter proposes that the combination of psychology and ethology can contribute to our understanding of dog-human attachment and opens the door to create testable hypotheses and predictions regarding dogs' propensity to make strong 'affectional bonds' with us. The concept of attachment bond can be used to study different types of human relationships, and is also a plausible theoretical ground of developing ways to assess attachment in dog-human relationships, which might be used for studying some other species. Human-animal relationships, including those with dogs, can be interpreted in terms of different social frameworks entailing different research approaches. That is, depending on the attitude towards the species we bring to research, both the conceptual framework and the adopted methods will differ. The chapter provides evidence that dogs of low or restricted contact with humans may retain their ability to form new attachment relationships with humans. Keywords:attachment bond; conceptual framework; dog-human relationships; ethology; psychology
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".