Resilience and capacity in small island jurisdictions: a case study of adaptation for climate change on Prince Edward Island
Bibliographic record
Abstract
The purpose of this case study was to examine the constraints and opportunities that those involved in policy creation in Prince Edward Island perceive as enhancing or impeding their ability to implement climate change adaptation policy, specifically land-use policies that could assist communities in\npreparing for the impacts resulting from sea-level rise. Twenty-six key informants were questioned using an open-ended semi-structured interview guide. Specific objectives that were addressed in the questionnaire included: gaining an understanding of key informants’ perceptions of risk and the\nimportance of adaptation; providing insights into the adaptive capacity of select communities on PEI by examining past, present and future adaptation strategies; investigating issues of sub-national jurisdiction and governance to determine who is responsible for implementing adaptation strategies in\nisland communities; and determining whether aspects of islandness (resulting from operating in a small island environment) influence adaptive capacity by presenting constraints or opportunities for adaptation. The interview results were transcribed verbatim and analysed using qualitative techniques,\nspecifically framework analysis. Themes, concepts, and results are discussed using the theoretical framework of resilience thinking and using relevant island studies literature. Data analysis yielded two conceptual models that explain the interactions between common responses for barriers and\nopportunities for the creation of a land use policy for sea-level rise (SLR). One model demonstrates why there is an absence of land use policy for SLR while the other suggests how to utilise strengths and opportunities to move forward with the creation of a land use policy for SLR. The thesis concludes with\na summary of recommendations for how to move forward with land use policy for sea-level rise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".