Relocation as a possible response to climate change: exploring perspectives and lessons from the Town of Channel-Port Aux Basques
Bibliographic record
Abstract
Much of the world's population lives in coastal areas situated a few meters above sea level. However, over the past few decades, climate change has modified coastal life through recurrent extreme weather events and other biophysical processes, including erosion and sea-level rise. Together, these phenomena have the potential to make coastal areas less habitable and coastal communities more vulnerable, including displacing people. As a result, relocation has emerged as a potential and proactive adaptation option to climate change. However, planning for relocation is not without challenges as relocation has the potential to disrupt several aspects of individual and community life. Through document review and narrative interviews, this thesis studied climate-related relocation in the Town of Channel-Port aux Basques to explore relocation as an adaptation strategy and to mobilize local stories and perspectives in pursuit of lessons for coastal planning and policy. A strong sense of place, perception of risk, and grief over environmental loss or detachment were among the factors that would have made a voluntary relocation less tenable if Hurricane Fiona had not happened. These remain the reasons people unaffected by Hurricane Fiona are unwilling to move. Factors such as a lack of communication and community participation in the relocation decision-making process, long response time hearing back from officials on compensation packages, and inconsistencies in the determination of compensation packages were cited by participants to have contributed to their dissatisfaction with the relocation process. The research highlights the need for principles to guide future relocation policy development that is robust for varying communities, and relocation programs tailored to community needs and interests. To inform context-sensitive and engaged relocation planning, knowledge about local perspectives is needed in policy and planning. That knowledge should take into consideration the psychological, social, cultural, and economic heterogeneity in communities.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.014 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".