Eliciting the plurality of causal reasoning in social-ecological systems research
Bibliographic record
Abstract
Understanding causation in social-ecological systems (SES) is indispensable forpromoting change toward sustainable outcomes. However, studying causal relations in SES ischallenging because they are often complex and intertwined, and because different disciplines areinvolved in their analysis. While there is agreement in the field that no single research approach(RA) can comprehensively explain change in SES (or the absence of it), there is a lack of abilityto deal with this diversity. Underlying this diversity and the challenge to deal with it are differentcausal reasonings that are rarely explicit. Awareness of hidden assumptions is crucial forunderstanding how causal reasoning of a RA gets constituted, and to promote the integration,translation, or juxtaposition of different approaches. We identify the following elements asparticularly relevant for understanding causal reasoning: Methods, frameworks and theories,accounts of causation, analytical focus and causal notions. We begin with the idea that one of theseelements typically figures as an entry point to a RA. This entry point is distinctive and particularlyimportant because it generates a path dependence which orients causal reasoning. In a subsequentstep, when an approach is applied, causal reasoning concretizes as a result of a particularconstellation of the remaining elements. We come to these insights by studying the application offour different RA to the same social-ecological case (the collapse of the Baltic cod in the 1980s).On the basis of our findings we developed a guide for the analysis of causal reasoning by raisingawareness of the assumptions, key elements and the relation between these key elements for agiven RA. This guide can also facilitate the combination of RA for different purposes, such asgetting a more comprehensive and interdisciplinary understanding of causation, discoveringunknowns, generating learning, articulating theories, or simply supporting reflexivity.
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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 | 0.173 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".