Labor shortages and immigration: The case of the Canadian agriculture sector
Bibliographic record
Abstract
Abstract Reliable access to labor is an ongoing key concern for many employers, in particular for those in regions. As an attempt to help mitigate the effects of labor shortages, immigration has been deployed as a key strategy, but most immigrants are concentrated in large cities. A sector that represents an interesting case in point is the food production sector, which includes primary agriculture and food processing. We use a rich longitudinal micro‐database for the years 2001–2013 from the Canadian Employer‐Employee Dynamic Database to identify the factors that have an impact on the recruitment and retention of Canadian‐born and immigrant workers in the primary agriculture and food processing sectors. In particular, in response to the efforts to explore permanent residence pathways, whether or not immigrants with previous Canadian experience are more likely to stay in the sectors after entering remains a key question for policymakers that we investigate [EconLit Citations: J15, J18, J21, J63, Q10, Q12].
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".