Why Do Ethnic Citizens Identify More with Their Cultural Groups Than with the State in Canada?
Bibliographic record
Abstract
Abstract This chapter explores how social and public institutions influence ethnic groups’ identities in Canada. The chapter argues that historical, social, and systematic categorization of ethnic groups in Canada and their imagined communities of racialized minorities and citizenship provide arbitrary classifications that have undermined racialized immigrants’ citizenship rights, effective socioeconomic integration, and political participation. Data presented in this chapter are from transcribed stories of 120 participants interviewed between 2015 and 2018. The findings indicate that Canadian social and public institutions are influenced by systemic sociopolitical constructions of citizenship based on ethnocultural “facts” that arbitrarily link ethnic groups to a primordial identification with an imagined ethnocultural community. The chapter concludes that Canadian institutions should support ethnic communities’ agency to identify with the state by ensuring fair and equitable participation in all aspects of socioeconomic and political spheres.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".