Session 4: Escaping Violence Together: Housing pets in shelter with their loved ones
Bibliographic record
Abstract
Across Canada, few shelters currently support women and children to come into the residence with their family pets. Interval House of Ottawa has created an innovative and ground breaking project that supports women and children with pets to leave abusive relationships sooner and benefit from a pet’s companionship as they heal from trauma by entering the shelter with their furry, feathered and aquatic loved ones.\nIn order to respect the diversity of our clients and address allergies and fear of animals we decided to renovate our oversized basement. In this area we have fully enclosed spaces for dog housing; cat housing; small animal housing; a room for food preparation, sanitation and laundry; and a large storage room. To ensure good mental health of families and their pets, the area has day and moon light. It hosts two living rooms, with TV and Netflix, in which women and children can play with, and be comforted by their pets.\nAllergens and odors are contained with a separate HVAC system, and a designated commercial washer and dryer for animal bedding and towels. The animal housing area is soundproofed to ensure that barking or other noise does not reach the main living space of the shelter. It also hosts a designated entrance/exit for families with pets to use, so they can move directly from the animal housing area to outside. This secured door links the animal housing to an enclosed outdoor space for play and exercise.\nIn this presentation you will learn about: Building space within VAW shelters to house pets Attending to fears and allergies in a community living environment Getting started: partnerships, costs, proposals, funding options, insurance Operational policies and procedures needed
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.115 | 0.026 |
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".