Telling Fish Tales: The Role of Narratives in Social Ecological System Interventions
Bibliographic record
Abstract
“Social ecological systems” (SES) are circumstances where human activity and the natural world are interconnected and reciprocally influential. Ensuring these systems benefit the broader social and ecological communities is increasingly important as human activity grows. We aimed to provide novel insights about how workers make decisions about intervening in SES. We examined 32 SES fisheries interventions in British Columbia, Canada, through interviews and archival sources. We uncovered a narrative structure to those descriptions, wherein workers who were experts in fisheries management decided which purpose or purposes a system should serve, whether the system was serving those purposes, and what was causing any problems. These decisions then informed recommended interventions. We uncovered novel dimensions of those interventions, as well as what we termed “narratives of clashes,” where other stakeholders put forward differing accounts of what was happening in a SES. These clashes often forestalled the implementation of recommended interventions, with implications for the functioning of SES and how these workers felt about their jobs.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.054 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".