104 Our Diverse Cities The need
Bibliographic record
Abstract
Waterloo Region’s economy is one of the strongest and fastest growing in Canada. Its unemployment rate hovers around 5%. It has an insatiable need for a talented workforce to sustain and grow its technology-driven economy. Yet, as with many communities across Canada, Waterloo Region has failed to adequately facilitate the participation of immigrants into the local labour market (Janzen et al. 2003). To remedy this situation, various segments within the Waterloo Region community recently committed to working together. What follows is their story of collaboration. It is a story of intentionality, of collectively acknowledging the importance of immigrant employment to our community. And it is also a story of strategy, of collectively determining how best to move forward together in order to meet the challenges and realize the opportunities of an increasingly diverse workforce. Defining the problem Waterloo Region is no stranger to immigrant workers and entrepreneurs. Its commerce and culture have historically been influenced by the German-speaking immigrant work ethic and entrepreneurialism. Today the region is highly multicultural, with the fifth highest immigrant per capita population among urban centres across Canada, according to Statistic Canada’s 2001 Census. Yet evidence shows that immigrants in Waterloo Region perform below the labour force with respect to employment rates and income levels – despite having education levels than the employed labour force overall. According to Statistics Canada, in 2001 the unemployment rate for recent immigrants in Waterloo Region was 14%, compared to 5 % for Canadian-born individuals. Underemployment is also a major problem, with many of the skills needed in the community not being accessed. As a result, many immigrants have come to work in survival jobs, some contemplating a return to their homeland or relocating to other jurisdictions. This situation impacts negatively on immigrants and their families, on the local economy and on the health of the community at large. Clearly the underutilization of immigrant skills needed to be addressed. Identifying community assets Waterloo Region has a strong business sector. Four business associations lead the way: Communitech, the area’s technology business
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.208 | 0.047 |
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".