Empirical examples demonstrate how relational thinking might enrich science and practice
Bibliographic record
Abstract
Abstract Interdependent relationships among humans and nature often go overlooked, delaying better environmental, social and public health outcomes. Emerging approaches have emphasized thinking through relationships, which we call ‘relational thinking’. Threads of relational thinking have matured in areas such as anthropology and Indigenous scholarship, and interest is growing across many disciplines. Welcoming this new cadre of relational thinkers requires a more broadly accessible synthesis. Sustainability scholars have begun to overcome these barriers with high‐level overviews and broad calls to adopt relational thinking. This literature has investigated the conceptual underpinnings of relational thinking, but the concrete, empirical benefits of relational thinking for understanding human–natural systems remain obscure. Here, we introduce a wide range of accessible empirical examples to demonstrate the potential for relational thinking to illuminate diverse coupled human‐and‐natural systems. We complement these examples with an overview of the theory behind relational thinking. We use these empirical examples to argue that some conventional methods are consistent with relational thinking, particularly when accompanied by deliberation and flexibility about which relationships to target, why, and how. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".