CLIMIGRANT: An educational resource about climate change, human movements and health
Bibliographic record
Abstract
Abstract Background Climate change in combination with other social, economic, and political factors, can result in population displacement, relocation or migration, which may be forced or may be a voluntary adaptation strategy. For some, however, moving is not an option due to a lack of access to resources, capacity and power. These experiences have implications for health. Our objective was to develop an educational resource to raise awareness about climate change and their impacts on human movement and health. Methods This project was an initiative of the Quebec Population Health Research Network, in Quebec, Canada. A survey was distributed to 28 network members who have diverse expertise, including migration, climate change, environmental sciences, law, ethics, and public health, to identify key themes and messages to convey, and to gather supporting references and materials. A workshop was subsequently held with 10 survey participants to brainstorm ideas for the content, approach and format. A core working group then developed CLIMIGRANT in consultation and collaboration with a number of experts. Results Two illustrated stories (for a general public, including children) and an online course (for students, frontline workers, researchers and other stakeholders) were developed in English and French (available at Climigrant.org). Each provides an overview of how climate induced disasters or environment changes can result in inequitable and complex situations regarding migration/relocation. They also highlight the number of health consequences, both physical and psychological, and challenges related to addressing these. Solutions for prevention and adaptation are presented as well. The resource illustrates how anyone can be affected by climate change, regardless of where they live and conveys a message of solidarity and collective responsibility. Conclusions CLIMIGRANT can be used to inform and educate a broad population on climate change, human movement and health. Key messages • A diversity of methods can be used to raise awareness about climate change, human movements and health. • Anyone, anywhere can be impacted and displaced by climate change and we all have a role to play in addressing climate change and its impacts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.069 | 0.006 |
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".