Romancing 'the Rock': problematizing notions of welcoming immigrants and cultural diversification amidst strong Newfoundland identity structures
Bibliographic record
Abstract
Using Critical Discourse Analysis in a critical realist framework, I review the Government of Newfoundland and Labrador’s immigration strategy, CBC programming on immigrants, and the Lanier Phillips story. I explore how notions of the ‘true Newfoundlander’, ‘newcomers’ and ‘settlement’ are constructed. The contexts in which real or manipulated foundational myths of the Newfoundland experience are constructed often shows ‘Newfoundland culture’ as both a business and an often prescribed series of character traits and way of life developed in reaction to certain constructs of the ‘outsider’. Current narrow definitions of ‘economic’, ‘social’ and ‘cultural’ development, combined with homogenizing terms of ‘uniqueness’ and the persistent myth that all patriots who move away do so reluctantly, place immigrants in exoticized and marginalized positions whose ‘welcome’ may be limited and dependent on their ability to assimilate and act as proponents of these constructed images. This is offered as one reason why many immigrants may move, alongside non-immigrants, for reasons other than economic. This thesis illustrates a need for deeper study into the immigrant experience and a more emancipated discussion of how new people can be allowed to participate productively, justly and equitably in this province.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.033 | 0.114 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".