From global trends to local realities: A multi-scale scenario-building methodology for community infrastructure planning
Bibliographic record
Abstract
This paper details a multi-scale scenario-building methodology designed to explore transport infrastructure futures in rapidly changing Circumpolar North communities, bridging global trends and national contexts with local realities. Focusing on Churchill, Canada, and Kirkenes, Norway, we employed a hybrid approach that combined top-down adaptation of existing global and regional socioeconomic scenarios with bottom-up, participatory ethnographic research to ensure local relevance and incorporate stakeholder knowledge. We developed coherent scenarios across global, regional (national), and local scales, allowing higher-level archetypes to manifest differently depending on locally specific features identified through fieldwork. This consecutive, nested process utilized morphological analysis and the Factor-Actor-Sector framework to maintain consistency while accommodating local specificities. The methodology centered not just on scenario creation but also on the function of scenarios as a tool for community dialogue, utilizing artistic visualizations in workshops to engage diverse stakeholders. This approach demonstrates a way to navigate the tension between global and national drivers versus local community needs, yielding distinct yet comparable local futures grounded in broader development pathways. It offers practical insights for deliberations and planning in uncertain environments, both built and unbuilt.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".