Using qualitative scenarios to understand regional environmental change in the Canadian North
Bibliographic record
Abstract
This paper discusses the application of qualitative scenarios to understand community vulnerability and adaptation responses, based on a case study in the Slave River Delta region of the Northwest Territories, Canada. Three qualitative, graphic scenarios of possible alternative futures were developed, focusing on two main drivers: climate change and resource development. These were used as a focal point for discussions with a cross-section of residents from the community during focus groups, interviews and a community workshop. Significant overlap among the areas of perceived vulnerability is evident among scenarios, particularly in relation to traditional land use. However, each scenario also offers insights about specific challenges facing community members. Climate change was perceived to engender mostly negative livelihood impacts, whereas resource development was expected to trigger a mix of positive and negative impacts, both of which may be more dramatic than in the “climate change only” scenario. The scenarios were also used to identify adaptation options specific to individual drivers of change, as well as more universally applicable options. Identified adaptation options were generally aligned with five sectors—environment and natural resources, economy, community management and development, infrastructure and services, and information and training—which effectively offer a first step towards prioritization of “no regrets” measures. From an empirical perspective, while the scenarios highlighted the need for bottom-up measures, they also elucidated discussion about local agency in adaptation and enabled the examination of multi-dimensional impacts on different community sub-groups. An incongruity emerged between the suite of technically oriented adaptation options and more socially and behaviourally oriented barriers to implementation. Methodologically, the qualitative scenarios were flexible, socially inclusive and consistent with the Indigenous worldview; allowed the incorporation of different knowledge systems; addressed future community vulnerability and adaptation; and led to the identification of socially feasible and bottom-up adaptation outcomes. Despite some caveats regarding resource requirements for participatory scenario development, qualitative scenarios offer a versatile tool to address a range of vulnerability and adaptation issues in the context of other Indigenous communities.
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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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".