Speaking welcome: A discursive analysis of an immigrant mentorship event in Atlantic Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it