Attracting and Retaining Immigrants to Newfoundland and Labrador: \nVoices from the Newcomers and International Students
Bibliographic record
Abstract
This is a funded study facilitated by Memorial University’s Harris Centre looking at attraction \nand retention of newcomers and international students to NL. Study findings and \nrecommendations are summarized and compared to that of previous studies on immigration \nissue. It is our hope that this report will be useful to a variety of governmental agencies and nongovernmental \norganizations in formulating plan to attract and retain immigrants to meet the \nimpending needs of skilled human resource for economic development and prosperity of NL. \nThis was a multisite study with a total sample of 212, including 50 newcomers to NL, 83 \ninternational students, 36 immigrants who have left NL, and 32 newcomers from Toronto (27) \nand Montreal (5). Newcomers were initially defined as persons born outside of Canada who \narrived in Canada 5 years ago or less. The study received full approval from Memorial \nUniversity’s Human Investigations Committee (HIC) in January 2009. The data collection \nperiod was from February 1 to April 30, 2009. \nResearch Objectives: \nThis study addressed two questions: (1) What are the reasons for low attraction of newcomers to \nNL? (2) What are the factors that motivate immigrants to leave NL? The study incorporated \nboth qualitative methods (guided interview schedule and focus groups) and quantitative methods \n(structured survey questionnaires). There are also open-ended questions on the surveys which \nprovided additional qualitative data. The data gathered from different sources, using different \nmethods have been consistent and are complementary to each other. The triangulation of data \nsources and methods of data collection minimizes potential bias, avoids one-side interpretation \nand enhances the validity and reliability of the findings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".