Motivations and Narratives of Canadian Immigrant Influencers
Bibliographic record
Abstract
This research study uncovers the phenomena of social media influence within the Canadian legal immigration context. Aligning with an interpretative phenomenological approach, twenty-one personal semi-structured in-depth interviews were conducted with Canada-based Latin American social media influencers (SMIs) to access their lived experiences and perspectives. This study revealed how the immigration landscape and acculturation process encouraged Latino immigrants to undertake a role as opinion leaders and follow a professional journey in which the level of expertise defines the types of motivations and goals that drive them to perform this social media practice within the immigration approach. The findings present evidence of an intangible consumption context that is being digitally promoted as a lifestyle value proposition collectively co-created and communicated before, during, and after the immigration consumption process. Furthermore, this research explains how SMI’s content is structured with stories about their own experiences and cultural resources which are also developed as localized and customized narratives. Theoretical implications associated with the findings are offered, as well as future research, and managerial and public policy connotations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".