A Place-Based Approach to Sustainable Communities: A Case Study from Rapa Nui (Easter Island, Chile)
Bibliographic record
Abstract
The failure to account for temporal and spatial contingencies leads to attempts to apply universals to the needs and constraints of communities that may or may not be appropriate. Here, we argue that taking a place-based approach offers a way of incorporating local context to solve issues of sustainability at the scale of communities. We demonstrate this approach with Rapa Nui (Easter Island, Chile), a famous Polynesian island often associated with arguments for non-sustainability. Through detailed analyses of the island’s historical context and interviews with present-day governance leaders, we draw three conclusions. First, culture, governance, economy, environment, and equity, often described as “pillars” of sustainability, are inseparable and therefore better described as dimensions of sustainability. These factors are part of the place in which sustainable communities exist and must be integrated into analyses. Second, our findings demonstrate that we must adopt standards for sustainable communities that can change through time. What would have been considered sustainable in prehistoric times is no longer considered sustainable today. Third, globalization can be viewed as a driving force behind these changing views of sustainability. Furthermore, globalization has had both positive (e.g. access to health, education, and economic resources) and negative (e.g. threat to a culture of sustainability) impacts on Rapa Nui.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".