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Enregistrement W1587414220 · doi:10.55016/ojs/sppp.v7i1.42457

The Rise and Fall of Social Assistance Use in Canada, 1969-2012

2014· article· en· W1587414220 sur OpenAlexaffabout
Ronald D. Kneebone, Katherine E. White

Notice bibliographique

RevueThe School of Public Policy Publications · 2014
Typearticle
Langueen
DomaineHealth Professions
ThématiqueEmployment and Welfare Studies
Établissements canadiensTreasury Board of Canada SecretariatUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésDemographic economicsPolitical scienceEconomics

Résumé

récupéré en direct d'OpenAlex

Between 1994 and 2008, social-assistance usage rates across Canada fell at a remarkable rate, with the fraction of the non-elderly population drawing social assistance dropping by half over the 14-year period. Because social assistance can be considered the final layer of the public social safety net — designed to catch those people in need of support but unable to find it from family, friends or non-government agencies — such a dramatic decline in social-assistance usage deserves attention and explanation. Is it a positive sign suggesting that the country has made significant strides in keeping people from needing to receive social assistance or is it a sign that public policies have simply made it too difficult for those deserving of support to receive it? We do not try to answer these questions in this briefing note. Our goal is rather more modest; to simply draw attention to a dramatic fall in social assistance usage across Canada to levels not seen since the early 1970s. While the fall in social assistance usage has been observed right across Canada, the pattern and magnitude of change has varied by province. For example, despite being subject to similar economic forces, Ontario and Quebec have seen very different patterns in their respective social-assistance usage rates. In Ontario, social assistance use was traditionally much lower than in Quebec but this changed in the 1990s. Although both provinces suffered a serious recession in the early 1990s, the social assistance usage rate increased more and did so more quickly than in Quebec. In recovery, the social assistance usage rate has fallen steadily in Quebec and is today at the level it was in 1970. In Ontario, the social assistance usage rate fell but plateaued at a level higher than pre-recession levels. Today the rate in Ontario continues to climb, is higher than in Quebec, and is well above what it was in 1970. These two provinces, with similar economies but having quite different movements in social assistance use, offer an interesting comparison for those interested in evaluating each province’s policies toward social assistance. In the West, social-assistance usage rates also saw a long downward trend following dramatic increases in the mid1990s. Similar to elsewhere, usage rates in Western Canada saw only minor increases in response to the 2008 recession. By 2012, social assistance usage in all Western provinces had fallen well below that in Ontario and Quebec; in Alberta, for example, the rate is only half that in central Canada. Perhaps the most dramatic changes have occurred in the Maritime Provinces where social assistance usage is only half what it was just 15 years ago and currently sits below any level observed in those provinces since 1970. Remarkably, the rate of social assistance use in Nova Scotia, New Brunswick and PEI is currently below that in Ontario. In trying to explain these trends and interprovincial differences, researchers will surely focus on the fact that the timing of the dramatic fall in social assistance usage is very close to the federal government’s decision in the mid1990s to halt shared funding of social assistance with the provinces. The end of shared financing promoted provincespecific changes in social assistance policies and it is plausible to associate the changes in these policies with the fall in social assistance usage. What exactly were those changes in each province, and a balanced assessment of their impact, requires careful analysis. We do not perform that analysis in this report; what we have done is assemble and present the data on social assistance use that is a necessary prerequisite of that analysis.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,464
Score d'incertitude au seuil0,922

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,068
Tête enseignante GPT0,377
Écart entre enseignants0,309 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2014
Routes d'admission2
Résumé présentoui

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