"I arrived with just $1 in my pocket": Narratives of immigrant exceptionalism on X
Bibliographic record
Abstract
Narratives of immigrant exceptionalism such as “I arrived with just $1 in my pocket” are commonly used by established immigrants in Canada to signal their personal achievement, their resilience, their claims to legitimacy on Canadian issues, or simply to counter narratives of an unwelcoming place. The convenience of the temporal and seemingly causal communicative form of the narrative, further combined with the social position of the established immigrant, the narratives of immigrant exceptionalism are highly amenable for reproduction. These narratives must be contextualized within the wider discourse of Canadian exceptionalism that allow such narratives to be further reproduced over generations and cohorts of new immigrant arrivals through the creation of the imaginary integrated immigrant. The framing of the narratives of immigrant exceptionalism tells us about the spectrum of meanings that people attach to immigrant exceptionalism, as well as what Canadian exceptionalism means to them. I collected 165 posts from X/Twitter and categorized the tweets into frames. I find that relevant problem frames such as “taking responsibility” and “threat“ stand out, as well as benefit frames such as “solidarity”.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".