Choosing Canada: The Role of Brazilian Immigrant Influencers in Shaping Destination Reputation and Migration Decisions
Bibliographic record
Abstract
The widespread use of Information and Communication Technology (ICTs) has reshaped migration. Individuals with the agency to decide on a migration destination rely on social media platforms to guide their decision-making process. While scholars have highlighted the relevance of online spaces for migrants, there is a gap in exploring which digital actors facilitate migration and the type of information conveyed to aspiring migrants. This thesis studied the role of Brazilian immigrant influencers on Instagram in building Canada's destination reputation to shape co-national destination choices to fill this gap. For this purpose, this project relied on the content analysis of 30 Instagram posts from five Brazilian immigrant influencers and ten interviews with Brazilian newcomers residing in Canada. This thesis found that influencers convey an overtly positive representation of Canada, the 'Canadian Paradise,' by sharing partial and exaggerated information that compares life in Brazil and Canada. As a second finding, newcomers shifted their views after migrating and now believe that Brazilian immigrant influencers acted guided by economic motivations. These findings indicate that Brazilian immigrant influencers are digital migration intermediaries who rely on idealized representations of Canada to promote migration-related services, which reveals the emergence of a digital migration industry.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".