Barriers to Access – Policy Solutions from an Immigrant Perspective: The Policy Roundtable Mobilizing Professions and Trades (PROMPT)
Bibliographic record
Abstract
“What does a never-employed here, but out-of-funds mechanical engineer from Bangladesh desperately looking for sustenance write on his resume for a dish-washing job? ” (Khan 2002, p. 4) The question posed by Akbar Khan in his letter to the editor of a local community paper poignantly highlights the experience many immigrant professionals face in trying to employ their skills and expertise in Canada’s labour market. Since the introduction of the point system in 1967, hundreds of thousands of immigrants have been recruited to Canada for their skills. Canada’s inability to effectively integrate immigrants into the labour market is of increasing economic and social concern. Despite our dependence on immigration for labour market growth, studies repeatedly highlight the increasing marginalization and poverty of immigrants. In order to facilitate the effective integration of immigrants, innovative approaches and partnerships are essential to cut across the complexity of jurisdictions and intransigence of stakeholders. The voice of immigrant professionals and tradespeople is integral to these partnerships and to the development of solutions. In Ontario, the Policy Roundtable Mobilizing Professions and Trades (PROMPT) has been formed to provide that voice. PROMPT is made up of 20 immigrant professional and trade associations, community initiatives,
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.044 | 0.028 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".