For the Love of Darcie: Recognising the Human–Companion Animal Relationship in Housing Law and Policy
Bibliographic record
Abstract
This paper identifies the law’s failure to recognise and protect the human–companion animal relationship in the housing arena. The nature of the human–companion animal relationship has striking similarities to human–human relationships in the socially supportive aspects of the relationship such as attachment, nurturance and reliable alliance. This contributes to the social life and sense of well-being of the owner. There is also evidence that the human–companion animal relationship can have physical health benefits such as lowering the risk of death by cardiovascular disease. It is clear that society benefits from the human–companion animal relationship, which many owners perceive as akin to family, in the form of healthier, less isolated people with better social networks. Yet in the key area of housing, the law does nothing to protect or even recognise this relationship. In consequence, every year thousands of tenants in both the public and private sector are faced with ‘no pet’ covenants in their leases and grapple with difficulties such as reduced housing options, higher rents or the traumatic decision to give up their companion animal for rehoming or euthanasia. This is especially prevalent amongst vulnerable people, like the elderly and mentally ill, who are more likely to need to move into supported accommodation. This article examines housing law in countries, such as France and Canada, that prohibit ‘no pet’ covenants in residential leases and provides arguments for the effective formulation and implementation of such law in the UK.
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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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.053 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.026 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".