Internal Migration in the Canadian Prairies and British Columbia due to Climate Change
Bibliographic record
Abstract
The United Nations High Commissioner for Refugees (UNHCR) predicts that by 2050 there will be 25 million to one billion persons forced to migrate due to climate change (Becklumb, 2010), yet there is a distinct lack of research on the ripple effect that climate disasters will cause in regard to internal migration throughout Canada. While Canada may become a refuge for global citizens experiencing climate induced displacement, Canadians could also be forced to migrate internally. This paper will analyze the effects of climate change in western Canada including the Canadian Prairies and British Columbia and will explore the impacts of climate migration. Due to the fact that the Canadian Prairies contain only 18% of the Canadian population (Statistics Canada, 2022) but have 80% of the country's farmland, small groups of the population are responsible for cultivating Canadian produce. With climate induced weather phenomena, this small population could be forced to move away from their farms and change their livelihoods to live in cities when their land becomes barren. For those without the comfort of financial capital, migration will be the only way they can adapt to the effects of the climate crisis (Dickson et. al., 2016). Provincial and Federal governments have currently not released adequate ac
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".