Session 3: Preserving the critical bond between domestic violence survivors and pets: breaking the barriers
Notice bibliographique
Résumé
Human abuse and animal abuse are clearly linked. When it comes to domestic violence, as many as 70% of survivors report having a pet and that a pet was injured, maimed, killed or threatened in the last year. In addition, 48% of victims report delaying leaving out of concern for their pet. Despite the barrier pets pose in a victim's ability to leave, few domestic violence shelters house pets. RedRover helps survivors overcome this barrier through various grant programs, an online directory and outreach efforts that help victims and advocates find solutions.\nThis presentation will review examples of the types of co-sheltering, community partnerships and other programs domestic violence shelters have developed to ensure survivors can escape their abusive situations with their pets, as well as the resources and assistance RedRover provides, including the safeplaceforpets.org website, which contains a searchable database of over 600 programs and shelters throughout the United States and Canada that offer survivors assistance with finding temporary housing for their pets. Since 2012 we have helped over 60 shelters start or expand facilities for pets and provided over 500 grants for domestic violence survivors to board their pets while they stay at a domestic violence shelter. In October of this year, we are launching a campaign to increase awareness of safeplaceforpets.org among domestic violence victims, and I will provide a campaign update and have resources available to share.\nThrough our outreach work, surveys and conversations with advocates, we have also learned about the major obstacles that have historically prevented domestic violence shelters from housing pets. We will share these key obstacles and a few of the solutions created using strong community partnerships, along with an overview of what is offered in Allie Phillips' SAF-T Start-Up Manual, which is the guide we encourage people to follow to help answer questions regarding the various co-sheltering models and how to set them up.\nCurrently nine states in the United States do not have even one shelter that allows pets. RedRover's current outreach efforts have focused on these nine states, with the goal of having every U.S. state include at least one pet-friendly domestic violence shelter by 2022. In our most recent round of grant applications, we are happy to report that three of these nine states have applied! This presentation will touch on outreach strategies that have been effective, as well as open up a large or small-group discussions within the audience for ideas, connections and possible partnerships to address specific obstacles identified.\nRedRover is a 501(c)3 animal welfare nonprofit organization based in Sacramento, CA that works in the United States and Canada. RedRover believes pets are family, and we keep families together by helping animals and people in immediate crisis through our RedRover Relief and RedRover Responders programs, crises such as: natural disasters, veterinary emergencies or domestic violence. We also work to prevent animal cruelty and neglect by increasing empathy and understanding about animals through our RedRover Readers program.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,005 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,006 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,169 | 0,060 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».