Mi'kmaw lessons for realigning land relations in Bay of Fundy dykelands and tidal wetlands
Bibliographic record
Abstract
For the Mi'kmaq First Nation, the hypertidal Bay of Fundy has long been a source of sustenance, stories, and so much more. Starting in the 1600s, French settlers (locally called Acadians) converted much of the coast’s tidal wetlands to agricultural dykelands. Climate change currently threatens the diversified dykeland system, leading to complex decisions around how to adapt: restoring dykes to tidal wetlands, realigning (pulling back) dykes, and/or raising dykes in their current footprint. This community-engaged study aimed to document how the Mi'kmaq navigate coastal adaptation decision making on the Bay of Fundy coast, including how the Mi'kmaq value the focal landscapes and balance different adaptation approaches. Interviews with Mi'kmaq traditional knowledge holders and non-Indigenous key informants demonstrated nuanced perspectives on dykeland futures, recognizing some benefits of dykelands but pushing for tidal wetland restoration where possible, not just for the benefit of the Mi'kmaq, but for all relations, human and non-human. Mi'kmaw insights on relational governance, pluralistic decision making, and reconciliation through land-based action hold vital lessons for the Bay of Fundy and beyond.
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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 | 0.013 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.009 |
| 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".