Leveraging value-based pathways for Indigenous food security through participatory scenario planning: A case study from St. Paul Island, Alaska, USA
Bibliographic record
Abstract
Arctic Indigenous food systems face significant pressure, necessitating transformative changes to ensure food security. Given that Arctic Indigenous communities’ food security is disproportionally being affected by global change, it is important that they develop action plans focused on local agency to meet the challenges they face and leverage food system futures that align with local values. We employ a co-produced participatory scenario planning (PSP) approach that combines PSP with positive scenarios from seeds, leverage points (LP), and intergenerational dialogue to challenge traditional future prospects that tend to be pessimistic and support food security for the Aleut Community of St. Paul Island (ACSPI). Through focus groups, a PSP workshop, and a pathways workshop, this study (i) mobilizes intergenerational community values, develops (ii) four positive future scenarios grounded in these values, and (iii) five pathways to achieve a shared vision of the future food system, and (iv) identifies effective LP for real-world change. The community's shared vision represents a diverse, sovereign local food system rooted in Unangax̂ values. Pathways toward this vision include interventions for local capacity building, economic diversification, local agency, subsistence lifestyles, and community cohesion and health. The paper highlights the transformative potential of co-produced PSP in supporting Arctic Indigenous communities in envisioning and achieving sustainable futures. • Co-produced community vision and pathways for Indigenous local food system identified. • Indigenous food sovereignty and cultural revitalization central to food system vision. • Strategic pathways foster capacity, diversity, agency, subsistence, cohesion. • Community interventions link shallow and deep system levels for transformative change. • Recognizing cross-level interactions of interventions is key to leveraging change.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".