Identity, migration, and social media: Generation Z in USMCA
Bibliographic record
Abstract
Abstract Purpose This research analyzes national identity representations held by Generation Z youth living in the United States-Mexico-Canada Agreement (USMCA) countries. In addition, it aims to identify the information on these issues that they are exposed to through social media. Methods A qualitative approach carried out through in-depth interviews was selected for the study. The objective is to reconstruct social meaning and the social representation system. The constant comparative method was used for the information analysis, backed by the NVivo program. Findings National identity perceptions of the adolescents interviewed are positive in terms of their own groups, very favorable regarding Canadians, and unfavorable vis-à-vis Americans. Furthermore, the interviewees agreed that social media have influenced their desire to travel or migrate, and if considering migrating, they have also provided advice as to which country they might go to. On another point, Mexicans are quite familiar with the Treaty; Americans are split between those who know something about it and those who have no information whatsoever; whereas Canadians know nothing about it. This reflects a possible way to improve information generated and spread by social media. Practical implications The results could improve our understanding of how young people interpret the information circulating in social media and what representations are constructed about national identities. We believe this research can be replicated in other countries. Social implications We might consider that the representations Generation Z has about the national identities of these three countries and what it means to migrate could have an impact on the democratic life of each nation and, in turn, on the relationship among the three USMCA partners. Originality/value As one of the few studies carried out on USMCA national identities and by qualitatively exploring the representations that Generation Zers have about them, it may provide information that could contribute to expanding understanding among the citizens of the region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".