Introducing Canada's expression of interest model - the early shortfalls of express entry
Bibliographic record
Abstract
Trying to wrap one’s head around Canada’s rapid (and frequently changing) immigration system can leave you breathless (Alboim & Cohl, 2012). From temporary entry limitations, to new citizenship legislation, and increased ministerial powers, immigration policy changes have continuously been reshaping Canada’s future. During a House of Commons session in late 2013, Ms. Maria Welbourne, Senior Director of Strategic Policy and Planning of the Department of Citizenship and Immigration, provided an overview of an Expression of Interest (EOI) model approach, a modernization initiative which was coming to Canada to facilitate a faster (and more flexible) immigration system (House of Commons, 2013). Fast forward just over one year later and the EOI model, already in place in New Zealand and Australia, is in full effect as Canada’s new Express Entry system (Bellissimo, 2014). While chatter and speculation of the now fully-automated electronic application management system existed prior to its inception, the discussion since the January 1, 2015 start date has grown, raising many questions as to whether this new system will in fact achieve Canada’s economic objectives, primarily reducing application backlogs and coordinating application volume, and selecting those immigrant candidates who are expected to meet Canada’s economic needs (Richard, 2014). Key Terms: Expression of Interest (EOI), Express Entry (Pool), Ministerial Instructions, Economic Classes
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".