Finding mobility in place attachment research: lessons for managed retreat
Bibliographic record
Abstract
Climate change will affect many global landscapes in the future, requiring millions of people to move away from areas at risk from flooding, erosion, drought and extreme temperatures. The term managed retreat is increasingly used in the Global North to refer to the movement of people and infrastructure away from climate risks. Managed retreat, however, has proven to be one of the most difficult climate adaptation options to undertake because of the complex economic, social-cultural and psychological factors that shape individual and community responses to the relocation process. Among these factors, place attachment is expected to shape the possibilities for managed retreat because relocation disrupts the bonds and identities that individuals and communities have invested in place. Research at the intersection of place attachment and managed retreat is limited, partially because these are complicated constructs, each with confusing terminologies. By viewing the concept of managed retreat as a form of mobility-based climate adaptation, this paper attempts to gain insights from other mobility-related fields. We find that place attachment and mobility research has contributed to the development of a more complex and dynamic view of place attachment: such research has explored the role of place attachment as either constraining or prompting decisions to relocate, and started to explore how the place attachment process responds to disruptions and influences recovery from relocation. Beyond informing managed retreat scholars and practitioners, this research synthesis identifies several areas that need more attention. These needs include more qualitative research to better understand the dualistic role of place attachments in decisions to relocate, more longitudinal research about relocation experiences to fully comprehend the place attachment process during and after relocation, and increased exploration of whether place attachments can help provide stability and continuity during relocation.
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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 | 0.087 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.014 | 0.035 |
| Scholarly communication | 0.015 | 0.045 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".