Articulating Indigenous Futures: Using Target Seeking Scenario Planning in Support of Inuit-led Fisheries Governance
Bibliographic record
Abstract
Futures thinking is an increasingly popular approach to solving complex environmental problems because it offers a framework to consider potential and desirable futures. It is also possible to create highly participatory future planning processes that incorporate the perspectives, beliefs, and values of resource users. In 2019, a group of fisheries stakeholders in Nunatsiavut, an Inuit land claim region in northern Labrador, began a target seeking scenario planning process to help them create a vision for the future of commercial fisheries in the region. Through this process, the group hoped to not only create a vision of Inuit-led fisheries but also to advance communication, collaboration, and learning for the group. In this paper, we reflect on the process we underwent over the past few years, including the research design, data collection and analysis, and the results of the project to broadly consider the strengths and weaknesses of participatory scenario planning for Indigenous governance. Reflecting on the process that we undertook provides important, experience-based knowledge for future projects. The elevation of Inuit voices makes this vision specific to the region and reframes fisheries as a tool for cultural and political rejuvenation in the region.
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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 | 0.046 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".