The Intersecting Dynamics of Social Exclusion: Age, Gender, Race and Immigrant Status in Canada’s Labour Market
Bibliographic record
Abstract
Through the use of a social exclusion framework and analysis of recent data from the Survey of Labour and Income Dynamics (2009), a national longitudinal database, this empirical research investigates the mechanisms through which social groups are made and socio-economic outcomes are determined in Canada today. Our objective is to explore and describe the social characteristics and personal attributes that intersect to direct divergent economic realities. To this end, we initially present a brief review of the social exclusion literature, as well as descriptive data on several aspects of age and immigration. This is followed by logistic regressions for five dimensions of economic exclusion, to examine who is made socially excluded in economic terms in Canada. Subsequently, to progress the analysis from a focus on the individual effects of specific social attributes, we calculate the combined odds of two dimensions of economic exclusion (low individual earnings and insecure employment) for eight prototypes of individuals, to highlight the intersecting effects of social dynamics related to age, gender, visible minority status and immigrant status, and to ultimately explore who gets ahead and who falls behind in the Canadian labour market. We conclude with a discussion of policy and research implications. À partir d’un cadre d’exclusion sociale et de l’analyse de données récentes de l’Enquête sur la dynamique du travail et du revenu de 2009, une base de données longitudinale et nationale, cette recherche empirique étudie les mécanismes de formation des groupes sociaux et d’identification des enjeux socio-économiques d’aujourd’hui au Canada. Notre objectif est d’explorer et de décrire les caractéristiques sociales et les attributs personnels qui s’entrecoupent pour adresser des réalités pécuniaires divergentes. À cette fin, nous présentons d’abord une brève revue de la documentation sur l’ostracisme social, ainsi que des données descriptives sur plusieurs aspects de l’âge et de l’immigration. Puis, nous en venons aux régressions logistiques pour cinq dimensions de l’exclusion économique, afin d’examiner qui est socialement l’objet d’un rejet en termes financiers au Canada. Ensuite, pour faire avancer l’analyse à partir d’une focalisation sur les effets individuels d’attributs sociaux spécifiques, nous calculons les probabilités de deux composantes de cette exclusion économique (de bas revenus individuels et un travail précaire) pour huit prototypes d’individus, afin de mettre en lumière les effets croisés de dynamiques sociales reliées à l’âge, au genre, au statut de minorité visible et à celui d’immigré, et pour finalement explorer qui passe devant et qui reste en arrière dans le marché du travail. Nous concluons avec un examen des implications à étudier dans les domaines des politiques et de la recherche.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".