Are Immigrants the Future of the Canadian Agricultural Workforce?
Bibliographic record
Abstract
This paper examines the integration of the immigrant population as a probable future of the Canadian Agricultural Workforce. According to the policy literature, a shortage of labourers will affect food security within Canada and impact Canada’s global food export. At the same time, the Canadian agricultural sector recognizes an increase in immigrants who might be agriculturally qualified and/ or interested in entering the agricultural sector. Theoretical \nperspectives on this topic suggest that current immigration policies and conditions need to be improved to attract agricultural experts to the country and transition Temporary Foreign Works (TFWs) already in the country to permanent residency. Funding is also vital to improving training and human resources management in the agricultural sector. This major research paper addresses this situation and discusses the role of stakeholders such as the government, including federal, provincial, and municipal levels, in brokering innovations that bring immigrants as workers into the Canadian agricultural sector. This paper identifies and discusses steps to overcome existing barriers and each player’s role in overcoming them. Important findings and actions taken by some non-governmental leaders in Labour Market research such as the Canadian Agricultural Human Resource Council (CAHRC) will also be discussed. The context of this study is within Ontario with supplementary sources from across the country and the world and presents data from the past five to 10 years keeping in mind the global effects of the COVID-19 pandemic. Agriculture will be central to Canada’s future health and prosperity by decreasing the gap in agricultural labour shortages as Canadian farms feed the world and grow Canada’s economy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".