The Empirical Measurement of a Theoretical Concept: Tracing Social Exclusion among Racial Minority and Migrant Groups in Canada
Bibliographic record
Abstract
This paper provides an in-depth description and case application of a conceptual model of social exclusion: aiming to advance existing knowledge on how to conceive of and identify this complex idea, evaluate the methodologies used to measure it, and reconsider what is understood about its social realities toward a meaningful and measurable conception of social inclusion. Drawing on Pierre Bourdieu’s conceptual tools of social fields and systems of capital, our research posits and applies a theoretical framework that permits the measurement of social exclusion as dynamic, social, relational, and material. We begin with a brief review of existing social exclusion research literature, and specifically examine the difficulties and benefits inherent in quantitatively operationalizing a necessarily multifarious theoretical concept. We then introduce our conceptual model of social exclusion and inclusion, which is built on measurable constructs. Using our ongoing program of research as a case study, we briefly present our approach to the quantitative operationalization of social exclusion using secondary data analysis in the Canadian context. Through the development of an Economic Exclusion Index, we demonstrate how our statistical and theoretical analyses evidence intersecting processes of social exclusion which produce consequential gaps and uneven trajectories for migrant individuals and groups compared with Canadian-born, and racial minority groups versus white individuals. To conclude, we consider some methodological implications to advance the empirical measurement of social inclusion.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.026 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".