Local Conditions and the Prevalence of Homelessness in Canada
Notice bibliographique
Résumé
In 2018, the federal government coordinated point-in-time counts in 61 Canadian communities. These counts, all conducted over the course of a few nights during the months of March and April, revealed that 25,216 people were experiencing homelessness. Of those, 20,803 slept in emergency shelters while 4,481 slept on the streets, in cars, or in some other unsuitable place. Reviewing the data for 49 of those 61 communities, this paper examines the impact of community-level conditions on the prevalence of homelessness. The structural determinants of both sheltered and unsheltered homelessness are examined. The analysis shows that more expensive low-quality rental units have a strong positive relationship with the numbers of people staying in homeless shelters. A higher percentage of people in a community living in poverty is also related to increased numbers of people having to make use of homeless shelters. Increases in social assistance income, which undoubtedly improved the well-being of recipients, had no significant relationship with the number of people experiencing homelessness. This latter result is consistent with individuals and families with low income having a small income elasticity of housing demand. For these individuals and families, marginal additions to income are first used to relieve constraints on their budgets for food, utilities, and other necessities rather than being used to finance improvements in housing conditions. The fraction of the population that self-identifies as Indigenous is positively related to both sheltered and unsheltered homelessness, a result consistent with claims of discrimination in housing markets. Finally, a milder climate is associated with higher numbers of people experiencing unsheltered homelessness. These results suggest the most effective policy response to addressing homelessness is to lower the cost of shelter, an outcome best achieved by increasing the supplyof shelter that can be afforded by individuals and families with limited income. Tothis end, public policies directed toward reducing the cost of construction, policiesthat include reviewing density restrictions and land-use regulations and offering tax incentives, can be effective. Preventing the disappearance of single-room occupancy hotels, boarding houses, trailer parks and other forms of housing affordable to people with limited income are other policy responses likely to be associated with decreasesin homelessness. Increasing the stock of government-owned housing is another policy option, one best suited for providing housing for people whose homelessness is caused or exacerbated by disability, mental illness, substance abuse or other health issues requiring other support services. Marginal increases in income support, while important for increasing the well-being of individuals and families with limited income, are unlikely to be associated with decreases in homelessness unless they are sufficiently large to significantly reduce rates of poverty in the community.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».