Taking a life Course Perspective on Social Assistance use in Canada: A different approach
Bibliographic record
Abstract
Abstract
 
 In this paper we argue that a life course perspective on social assistance use in Canada can offer a more nuanced theoretical understanding of both individuals’ experiences and the importance of social structure, than more traditional sociological or economic approaches to welfare use. We also propose that examining social assistance use in this way does not require longitudinal quantitative or qualitative data, as is sometimes suggested, but that cross-sectional quantitative and qualitative data can be interpreted through a life course lens. We demonstrate this by examining the covariates of social assistance receipt using cross-sectional quantitative data from the Survey of Labour and Income Dynamics, and by analysing qualitative interviews with recipients about the process of beginning and barriers to ending benefit receipt. These analyses show not only how the cross-sectional data can easily be considered from the perspective of the life course, but also how this perspective provides a more satisfactory understanding of how social assistance polices can be thought of as both providing resources that are important in individual decision-making and as shaping lives. 
 
 
 Résumé d’article
 Dans cet article, nous montrerons que le paradigme du parcours de vie sur le bien être social au Canada peut offrir une compréhension théorique plus nuancée des expériences des individus avec ces programmes qu’une approche traditionnelle sociologique et économique face à l’usage du bien être social, tout en reliant cet usage à de plus larges structures sociales. Nous proposons aussi qu’examiner l’assistance sociale de cette façon ne requiert pas de données longitudinales quantitatives ou qualitatives, tel qu’il l’est parfois suggéré, mais que des données quantitatives et qualitatives qui se croisent peuvent être interprétées à travers la loupe de la durée d’une vie.
 Nous démontrons ceci en examinant les covariantes de la réception à l’assistance sociale, covariantes agencées en se servant des données quantitatives sectionnelles tirées du Enquête sur la dynamique du travail et du revenu (EDTR) et en analysant les interviews qualitatives des bénéficiaires à propos de leurs démarches initiales et leurs obstacles jusqu’à la réception finale de leurs bénéfices. Ces analyses montrent non seulement que des données sectionnelles qui se croisent peuvent être facilement considérées du point de vue de la perspective du cours d’une vie, mais aussi comment cette perspective fournit une compréhension plus satisfaisante de la façon dont on peut voir la double importance des politiques qui offrent des ressources aux individus et qui changent leur vie.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".