Narratives of Nature: The Work of Social Ecological System Intervention
Bibliographic record
Abstract
There are many workers whose responsibilities include overseeing “social ecological systems” (SES): circumstances where human activity and the natural world are interconnected and reciprocally influential. We aimed to learn about how workers decide to intervene in these systems, decisions that can shape the fate of ecological systems and the species within them, as well as the livelihoods and experiences of the people who interact with them. Based on data gathered from interviews with recreational freshwater fisheries managers in the Canadian province of British Columbia and from archival sources, we analyzed descriptions of 26 SES interventions. We uncovered a narrative structure to those descriptions, comprised of four processes wherein workers considered (1) which aspects of the system were valuable and important (which we termed valorization); (2) whether a problem existed (problematization); (3) what was causing the problem (untangling); and (4) which action to take in response (implementation). The overall narrative provided both a series of steps to guide interventions, and a rhetorical structure to justify them. However, other actors in the social systems sometimes had differing accounts of what was happening, leading to what we termed “narrative disjunctions.” We explicate the implications of our findings for the critical work of SES management.
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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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.071 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".