Review of Alberta's Provincial Immigrant Nominee Program: Success and Challenges
Bibliographic record
Abstract
The study evaluated the Alberta Immigration Nominee Program (AINP) to address its relevance and performance. The methodology consists of a review of Alberta’s PNP program over the last 10 years. A case study approach is used, drawing from provincial government sources, statistics and non-traditional sources such as agency websites dealing with immigration in Alberta. The time frame for the evaluation is 2002 to 2012. The data used in this research study was provided by the Ministry of Enterprise and Advanced Education of Alberta. The data analysis is derived from the data set made available to the author by the Ministry. The AINP is administered by the ‘Workforce Strategies’ department in the Ministry of Enterprise and Advanced Education. Unlike other PNPs that seek population growth, the AINP is focused on addressing employer needs in Alberta and attracting a skilled workforce to strengthen labour shortages in key industries. AINP is a medium scale program, with an annual nomination of 4,000 applicants per year, which constitutes 10 percent of the annual immigrant settlement in Alberta. The AINP has grown a significantly in the last 10 years, from just over 6 people admitted in 2002 to almost 9,183 (principal and dependents) in 2012. Provincial nominees accounted for 33 percent of economic class admissions and 23 percent of total immigration to Alberta in 2010. Upon examining the characteristic of AINP nominees, immigrants entering the province through this program had a high level of education: 50% of AINP nominees had a Bachelor’s degree or higher when they were nominated. The mean income of AINP nominee was higher than that of other provincial PNP nominees. According to CIC, at the end of the first year, AINP nominees earned an average of $79,000. Most of the AINP nominees were of the prime working age group, and the share of female nominees is increasing. In the last two years, 30 percent of nominees were female. From 2002 to 2012, most of the AINP nominees were from the Asia-Pacific region. Philippines, China, the UK and India are a significant source of AINP nominees, together accounting for over 40 percent of AINP nominees. The literature on the AINP remains scarce. This study offers a preliminary observation on the AINP. It provides a background for the further research on the AINP. Alberta is starting to grapple with policy questions on immigration. This study hopes to provide a starting policy note for further research and debate on the different available options to improve program efficiency.
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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 | 0.023 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".