“We don’t have a lot of trees, but by God, do we have a lot of fish”: imagining postcolonial futures for the Nunatsiavut fishing industry
Bibliographic record
Abstract
Telling stories can be an empowering exercise, providing important insights into the values and priorities of the storytellers. This article shares stories told during a participatory scenario planning process among Inuit, an Indigenous People of northern Canada, Greenland, and Alaska, USA. This research takes place in Nunatsiavut, a land claim area in Labrador, Canada, to explore how visions provide insights into postcolonial futures for the fishing industry. Beginning in 2019, a group of fisheries stakeholders and managers came together to create a visioning process that would help them to develop consensus around priorities for the industry. Facilitated by university researchers, Inuit in the commercial fishing industry participated in an iterative data-collection process that involved interviews and a workshop. This article shares what was found during the scenario-planning process and position stories of the future within the context of Indigenous sovereignty.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.022 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".