Strategic Foresight in Métis Communities: Lessons from Indigenous Futurism
Bibliographic record
Abstract
In this paper, I explore and critique the use of strategic foresight methods with Métis Nations and consider the possibility of Indigenous futurism for decolonizing the field. Working in collaboration with the Métis Nation of Ontario to explore their future as a self-governing nation, I assess the suitability of the Three Horizons foresight method in an Indigenous context. Collecting data from \na facilitated Three Horizons workshop and a focus group session, the paper follows an Indigenous methodological approach. My findings show that while the Three Horizons method was robust in engaging the Nation around this subject matter, futurists must revisit the mental models from which they approach futures studies. Concepts and lessons from Indigenous futurism could challenge futures practitioners to explore new understandings. I conclude by arguing that to avoid colonizing the future, futurists must make space for foresight practices that are community-led, privilege Indigenous voices, and shift power away from expert-led dialogues.
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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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.030 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".