An exploration of Indigenous‐settler relations in the Port Alberni Valley, British Columbia regarding implementation of the 2011 Maa‐nulth Treaty
Bibliographic record
Abstract
Abstract On April 1, 2011, the Maa‐nulth Treaty went into effect; this treaty involves signatories from five First Nations, the Province of British Columbia, and Canada. Encompassing territories never before ceded—largely in the Port Alberni region of Vancouver Island—these First Nations have reclaimed a degree of self‐determination through the Treaty. Using it as a platform for analysis, this study sought to examine local Indigenous‐settler relations within a modern treaty context. During the week of Treaty celebrations and formal implementation, face‐to‐face semi‐structured surveys were administered to local residents of Port Alberni, asking their perspectives on the Treaty to determine the breadth and depth of comprehension—and tensions surrounding it. Our hypothesis was that in the heart of the region where impacts would be felt the strongest, there would be diverse (likely heated) opinions revealed through the data; to our surprise, however, over 40 percent were unaware of the Treaty's existence, let alone its implications. This resulted in some challenges in the extent to which our data could be interpreted but provided proof‐of‐concept for further exploration into why residents remain unaware of their own implication in modern treaty negotiations and their associated historical complexities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".