Bibliographic record
Abstract
For centuries, dogs have played a key role in the lives of humans both as companions as well as working animals. In recent years, the value of dogs in environmental work has been documented in the literature—namely their ability to detect targets more efficiently than humans and equipment. However, the environmental work dogs perform in Canada has been largely understudied in terms of both the specific tasks they are responsible for, as well as their welfare within these roles. This chapter addresses those gaps through an exploration of whether conservation canines could be an example of a humane job—one that is good for people, animals, and the environment. To do so this chapter explores tangible and moral issues related to dogs’ enjoyment of and suffering within conservation work, highlighting the complexity of dogs’ work-lives related to issues of freedom and consent. Findings are presented from two main case studies: Alberta and Ontario. An ethogram was used to assess dog welfare, while semi-structured interviews and participant observations revealed further insights into dogs’ work and work-lives. Ultimately, this chapter offers a discussion regarding how the study’s findings might inform assessment of humane jobs and work-lives, offering enjoyment, control, agency, respect, and recognition for dogs in this sector and for possibilities of fostering interspecies solidarity in other areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.012 |
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".