Nova Scotia's immigration strategies: Tapping into the potential of international students
Bibliographic record
Abstract
The socio-demographics of Nova Scotia, Canada provide an unfavourable outlook for the economic future of the province. Nova Scotia is faced with low birth rates, a large aging population and a high rate of youth out-migration, factors that are predicted to give rise to labour shortages and to destabilize the economy. The negative economic predictions regarding the future of the province would significantly damage the prosperity and quality of life of Nova Scotians. International students who reside in Nova Scotia to pursue their studies have the potentialto offer the province an ideal opportunity to revitalize communities across the province. However, this thesis argues that there are persistent barriers that impede the recruitment, attraction and retention of immigrants in Nova Scotia. Unwelcoming and exclusive attitudes towards immigrants and a lack of supports and services provided to newcomers are argued to be among the main factors contributing to low attraction and retention of immigrants in Nova Scotia. Focusing on the retention of international graduates, this thesis employs a case study of the international student population at Acadia University, concluding that in order to increase immigration in Nova Scotia, partnerships must be forged between the Government of Nova Scotia, the Federal Government, post-secondary institutions in the province, local communities and the private sector, with the common goal of increasing immigrant attraction and retention in the province.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".