Bibliographic record
Abstract
Studies on refugee reception in third tier cities and rural areas generally agree that refugees often leave these less densely populated areas in favour of major metropolitan areas. The first two years of residence are key to retention. If the refugee has not left the area by then, they are much more likely to stay permanently (Carter, Morrish, & Amoyaw, 2008; Donato, Tolbert II, Nucci, & Kawano, 2007; Fonseca, 2008; Hugo, 2008; Krahn, Derwing, & Abu-Laban, 2005). New Brunswick is the only province in Canada with a declining population. The provincial government has made it clear that it considers the demographic issue a primary concern (Government of New Brunswick, 2014), and sees refugee reception as a potential way to break this trend. Retention of the accepted refugees is thus a particularly prioritized issue here. This paper details refugee experience of settling in New Brunswick, showing issues refugees identifies as barriers to settlement, as well as the suggestions the respondents presented as potential solutions. They discussed their foreign work experience, the services they appreciated, the primary barriers to employment, their suggestions for solutions and finally their reflections on whether they are going to stay in New Brunswick or not.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".