Recognizing “reciprocal relations” to restore community access to land and water
Bibliographic record
Abstract
Place-based communities are struggling to maintain their connections to land and water, including the social and cultural practices that are rooted in a particular landscape. In this paper, we consider possibilities for recentering environmental governance around reciprocal relations, or the mutual caretaking between people and place. We draw from existing scholarship on relational values and human-nature relations, which emphasize the intrinsic value and agency of non-human beings and the landscape itself. By linking key concepts in the literature to our four case studies, we develop a framework of reciprocal relations as a foundation for local practices and governance policies that facilitate increased community access to land and resources. Our cases investigate the practice of reciprocal relations across different community contexts in Hawaiʻi, British Columbia (Canada), the Appalachian Mountain Region (U.S.), and Madagascar. Through our analysis, we examine a diverse range of community approaches to reciprocal relations, and demonstrate how practicing reciprocal relations can have material effects on community well-being and environmental sustainability. This finding builds on the theory of access (Ribot and Peluso 2003), by suggesting that practicing reciprocal relations can provide a powerful mechanism for shifting community access to resources. In the reciprocal relations context, however, the flow of benefits is not uni-directional. Expanding on existing access concepts, we show how the ability of a place-based community to benefit from resources is contingent upon its ability to maintain multi-directional and mutually beneficial relations with the natural environment—in part through fulfilling caretaking responsibilities for land and water.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| 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".