Extrinsic Learning, Corporate Streaming, and Ungrounded Voting: The Role of STEM Schooling in the Political Socialization of Asian Canadian Youths
Bibliographic record
Abstract
This ethnographic study analyzes a collection of schooling, childhood, and migration narratives from Asian Canadian youths who have entered careers related to Science, Technology, Engineering, and Mathematics (STEM).The study centres on a group of Asian migrant engineering alumni from a major Canadian university, and unpacks the relationship between their STEM-based scholastic socialization and their political consciousness in civic life.Through the use of qualitative methods involving semistructured interviews supplemented by neighbourhood walking tours, the data provides a humanizing portrayal of the classed and gendered dimensions of petty bourgeois migrant life.Employing a Bourdieusian framework, analysis of the data reveals that symbolic homologies related to the fundamental tension between economic and cultural capital underpin many of the mundane tensions found in the participants' life narratives.This tension between economic and cultural capital exerts effects across multiple phases of the participants' formative years, including high school as well as university and beyond.The side that one takes in this clash of capitals is homologous with their decision to enter STEM, the subjective meaning of their discipline, the learning styles they adopt within the discipline, and the political tendencies they develop upon entering the workforce.Psychosocial analysis of their homological schema suggests that conservative political tendencies amongst the voters in this demographic stem from the inaccessibility of civic engagement, especially the inaccessibility of grounded politics in which one can see oneself represented in one's cause.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".