Education and employment training supports for newcomers to Canada’s middle-sized urban/rural regions: Implications for social work practice
Bibliographic record
Abstract
The last decade has witnessed the movement of immigrants from Canada’s largest urban centers—Toronto, Vancouver, and Montreal—to smaller urban-rural communities. Nevertheless, very little scholarship exists on newcomer integration in these communities. Furthermore, social work literature examining the perspective of service providers who work with newcomers is lacking. Grand Erie is a middle-sized urban/rural region in Ontario, Canada that is experiencing increased migration of newcomers. This paper focuses on a part of a larger Community-based participatory research on ‘Newcomer Settlement and Integration in Education, Training, Employment, Health and Social Support’ in Grand Erie and discusses the findings in the education and training domain. Data were gathered from 212 newcomers (men and women) and 237 service providers using survey questionnaires. Findings Most of the newcomers in this study had not taken any education or employment courses post-migration. The qualitative and quantitative responses from participants (newcomers and service providers) highlight a lack of affordable child care and poor transportation infrastructure in this region as significant barriers to newcomers’ ability to take education or employment courses especially in case of visible minority women. Applications The results of the study suggest that there is an opportunity for social workers to build partnerships with community agencies as well as with policy-makers at regional and provincial levels to foster the social, economic, and political integration of new immigrants in the host society.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".