Thinking Outside the Nation: Cognitive Flexibility’s Role in National Identity Inclusiveness as a Marker of Majority Group Acculturation
Bibliographic record
Abstract
In superdiverse societies like Canada, characterized by high levels of cultural and ethnic plurality, national identity boundaries are often blurry. While policies may officially promote inclusiveness, public discourse on national identity is frequently dominated by mainstream groups, whose willingness to expand these boundaries plays a crucial role in fostering minority inclusion. Despite the importance of inclusivity for social cohesion, little is known about what enables majority group members to adopt a more inclusive national identity. This study addresses this gap by exploring the role of cognitive flexibility in facilitating an acculturative shift toward inclusiveness. Using latent class regression analysis (N = 202), we identified two distinct national identity profiles: one more inclusive and the other more exclusive. We also examined how factors such as ethnic vs. civic views on national identity, acculturation orientations toward integration, and personal identification with traditional English Canadian vs. multicultural identity representation shape these profiles. Our findings revealed that higher cognitive flexibility was positively associated with the likelihood of belonging to the more inclusive profile. This study contributes to a limited body of work on majority group acculturation, offering insights into how cognitive flexibility may encourage a broader and more inclusive national identity. Implications for policy and social cohesion are discussed.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".