Opportunities for Employers in Immigrant Labour Market integration: a Case Study of the Canadian Financial Sector
Bibliographic record
Abstract
The focus of this study is on the financial sector, and it asks two questions: a) what are financial institutions currently doing in terms of assisting in the labour market integration of newcomers? and b) where are the opportunities for improvement within the financial sector with respect to the employment of immigrants? The study examines current achievements regarding the sector’s successful labour market integration of immigrants, opportunities for improvement, and recommendations as to how the financial sector can become a leader in this domain as well as the benefits of doing so. Some key findings are that there is a gap in terms of the successful integration and inclusion of immigrants in the labour market despite recognition of the business case for diversity. Ingrained biases and beliefs persist, and the communication patterns and ‘rigid’ history of the financial sector are not maturing at the same pace as the global economy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.044 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".