Exploring the Meaning of “Welcoming Communities” for \nImmigrants in Newfoundland and Labrador
Bibliographic record
Abstract
Atlantic Canadian communities consider immigration from outside of Canada to be a viable \nsolution to problems associated with its aging population and population decline, but this \nregion has had limited success in attracting immigrants who remain in the region throughout \ntheir lives (Akbari, 2005; Bruce, 2007). In 2007, the provincial government of Newfoundland & \nLabrador launched a Provincial Immigration Strategy designed to increase the number of \nimmigrants coming to and staying in NL. Like other Atlantic Canadian provinces, NL faces low \nbirth rates and an aging population with a high number of baby boomers ready to retire. In \naddition to these challenges, NL is poised to launch major resource development initiatives \nrequiring significant manpower. One aspect of the dilemma NL faces in attracting and retaining \nimmigrants that has not received a great deal of attention is the way in which communities \nwelcome immigrants, and how a welcoming community may enhance social engagement, and \none’s sense of inclusion in the host community which in turn may in turn lead to long term \nresidency. This research is part of a larger study of social engagement and inclusion of \nimmigrants in Atlantic Canada. The intention of this research is to begin the work on the NL part \nof the project by compiling community profiles in NL. The larger project has been submitted to \nthe SSHRC and to the Atlantic Metropolis Centre. \n
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.024 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".