Storytelling for sustainable development in rural communities: An alternative approach
Bibliographic record
Abstract
Abstract Mainstream conceptualizations of sustainable development (SD) tend to focus on urban areas or the national or global scale—most recently through the Sustainable Development Goals. This focus often overlooks rural and natural resource‐based communities, particularly those dependent on renewable resources like fisheries or forestry. Drawing from a comprehensive review, we propose an alternative approach for interpreting and measuring SD in these contexts. We integrate two seemingly contradictory approaches: sustainability indicators (SIs), whose evolution reflects competing views of the nature of knowledge and action in pursuit of SD, and the use of storytelling in policy and planning, highlighting how actors tell stories to garner support for proposed developments, influence public understanding, and mobilize stakeholders. Examining the opposing epistemologies often underlying these two approaches, we posit that they can be brought together through a transdisciplinary lens for sustainable rural development. We illustrate these potentials in Newfoundland and Labrador, a highly resource‐based region in which rural communities are often characterized by deficiencies based narratives. In such contexts, storytelling can allow rural stakeholders to interpret SD while potentially enlisting SIs in telling their own sustainability stories.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".