Coupling social and ecological mechanisms with the Coleman boat
Bibliographic record
Abstract
As in many other fields, mechanism-based theorizing has become increasingly popular in social-ecological research. However, calls for mechanism-based explanations and middle-range theorizing have remained relatively abstract. In the social sciences, the Coleman diagram provides heuristic aid to figure out mechanisms for macro-scale causal claims. The diagram, understood as a series of analytical questions, helps to connect macro processes and agents’ behaviors causally and to build understanding of how the macro effects get generated. This paper argues that the Coleman diagram can also be helpful in advancing mechanism-based theorizing in social-ecological research. Using an updated version of the diagram, we show how to incorporate ecological and social-ecological mechanisms into social explanations. The paper systematically explores how social and ecological mechanisms could intertwine with each other and illustrates them with brief examples. It also introduces the concepts of an action situation, mental states, and agent capacities to dissect the interface between agency and social-ecological change. Finally, the paper discusses how the diagram can integrate various forms of causal complexity. The ecologically expanded Coleman’s diagram contributes both to social-ecological research and social theory. It provides a concrete tool for integrating social and ecological theorizing using the idea of a mechanism-based explanation, and it also shows that mechanism-based theorizing is a viable avenue for developing more ambitious interdisciplinary theories about significant challenges both people and ecosystems face.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".