Bibliographic record
Abstract
Climate change is expected to affect Canada through extreme heat events (EHEs). Already vulnerable populations, including newcomers and immigrants, will especially be vulnerable to the health impacts associated with EHEs. This population is important to consider for a country as diverse as Canada. With a focus on Hamilton Ontario, this thesis will assess barriers that immigrants and newcomers face with coping to EHEs. Adverse impacts they face will also be discussed. Current formal and informal coping methods will also be highlighted. Quantitative analysis will also be used to explore the relationship between EHEs, air quality (as measured by the Air Quality Health Index (AQHI)), forward sortation areas and hospital admission for heat-related illnesses. The results of this study highlight that unique factors influencing heat health vulnerability among immigrants and newcomers in Hamilton. The benefits of current formal and informal coping mechanisms will also be discussed, as well as areas for improvement. Quantitative analysis also highlights that the AQHI, maximum temperature and a heat event can impact if an individual is admitted to the hospital for a heat-related illness. However, age, gender and most FSAs were not statistically significant. This thesis highlights the importance of considering the immigrant and newcomer population for EHE and general climate change adaptation efforts.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".