Harnessing the Power of Stories for Rural Sustainability: Reflections on Community-Based Research on the Great Northern Peninsula of Newfoundland
Bibliographic record
Abstract
Stories have the power to shape understanding of community sustainability. Yet in places on the periphery of capitalist systems, such as rural and resource-based regions, this power can be used to impose top–down narratives on to local residents. Academic research often reinforces these processes by telling damage-centric narratives that portray communities as depleted and broken, which perpetuates power imbalances between academia and community members, while disempowering local voices. This article explores the potential of storytelling as a means for local actors to challenge top–down notions of rural sustainability, drawing on a community-based research initiative on the Great Northern Peninsula (GNP) of Newfoundland. Five of the authors are community change-makers and one is an academic researcher. We challenge deficiencies-based narratives told about rural Newfoundland and Labrador, in which the GNP is often characterised by a narrow set of socio-economic indicators that overlook the region’s many tangible and intangible assets. Grounded in a participatory asset mapping and storytelling process, a ‘deep story’ of regional sustainability based on community members’ voices contrasts narratives of decline with stories of hope, and shares community renewal initiatives told by the dynamic individuals leading them. This article contributes to regional development efforts on the GNP, scholarship on sustainability in rural and remote communities, and efforts to realise alternative forms of university-community engagement that centre community members’ voices and support self-determination.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.031 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 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".