Deconstructing Human Capital Theory: The Silenced Narratives of Immigrant Women of Colour
Bibliographic record
Abstract
This study critically examines the limitations and inadequacies of Human Capital Theory (HCT) in addressing the employment realities of immigrant women of colour in Canada. Rooted in neoliberal ideology and neoclassical economics, HCT assumes that individual investments in education and skills directly lead to economic success. However, this framework fails to account for systemic barriers, structural racism, and gendered discrimination that disproportionately disadvantage equity-deserving communities. Applying Pierre Bourdieu’s concepts of social capital and habitus alongside critical race theory (CRT), this research interrogates HCT to examine how race- and gender-based exclusions, social networks, cultural norms, and institutionalized biases shape career trajectories. While intersectionality is not the central focus, the study integrates an intersectional analysis to highlight the compounded disadvantages faced by immigrant women of colour in the labour market. Employing Indigenous storytelling methodologies and narrative inquiry, this research foregrounds the lived experiences of immigrant women of colour to challenge the assumptions of HCT. By conducting a comprehensive literature review and examining CRT, the study further interrogates how race, power, and systemic exclusion intersect with economic mobility. Despite its widespread acceptance, HCT remains insufficient in explaining the persistent employment gaps and labour market marginalization of immigrant women of colour. This study argues that access to social capital is a critical determinant of career success, often privileging dominant groups while excluding racialized immigrants. The interplay between HCT and social capital theory creates compounded barriers, reinforcing systemic inequalities and limiting career advancement for marginalized populations. As Bourdieu’s work does not explicitly address racial disparities, this study integrates CRT to demonstrate how race fundamentally influences labour market outcomes. By bridging these theoretical perspectives, the research challenges dominant economic narratives and calls for policy reforms that recognize and dismantle structural barriers to economic equity for immigrant women of colour.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.030 | 0.031 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| 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".