Speaking welcome: A discursive analysis of an immigrant mentorship event in Atlantic Canada
Bibliographic record
Abstract
This article offers an analysis of a business mentorship event in Fredericton, NB, which targeted immigrants sponsored through the New Brunswick Provincial Nominee Program (NBPNP)—an economic revitalization program designed to attract foreign business people and skilled workers to settle in the province. Applying Derrida’s concept of hospitality as a technology of whiteness, we examine the stated and implicitly understood expectations for the NBPNP, including the mechanisms at play for regulating newcomer’s behavior and comportment. We locate our analysis in the context of a regionally expressed Canadian multiculturalism, extending the relevance of our findings beyond Fredericton to Atlantic Canada. We ask: how do associated discourses of whiteness, multiculturalism and hospitality come into play to shape dynamics of power existing between hosts (settlement workers, various shadow state actors and mentor volunteers) and racialized newcomer guests? As a racialized threshold event, the Sip, Greet and Meet facilitated an exchange of hospitality such that the New Brunswick native hosts marked newcomers as perpetual arrivants, while holding the immigrants responsible for the success of their settlement in the Fredericton region. We show how the discourses regarding newcomers’ duties cleared nativist inhabitants of any accountability for the success of immigrant settlement. We also show how the process of welcoming conveyed a message that the future success of the local community, the province and even Atlantic Canada depended on the business class immigrants’ ability to serve as dutiful and grateful guests.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.040 | 0.017 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".