“Unless you get to know us, you’re not going to know our passion”
Bibliographic record
Abstract
In British Columbia, First Nation consultation is formally operationalized through land referrals, a system by which governments and industry proponents share information with First Nations about specific developments on their territories. Using the lenses of equal capacity, social learning, and Indigenous knowledge, this research examines land referrals processes on Nadleh Whut’en First Nation Territory to explore experiences with current consultation practices. Despite efforts to move towards equal decision-making authority over First Nation Territories, consultation remains a key aspect of Indigenous-Crown relations and it is critical that planners enhance their understanding of the duty to consult. Interviews with Nadleh Whut’en knowledge holders and land users and forestry key informants highlight deficiencies in the referrals process as a vehicle for engaging communities in decision making. Challenges include short timelines that manufacture false consent and a lack of cross-cultural dialogue centered on community values and needs. This research fills a gap by advancing understandings of the technical and procedural details of current consultation practice, including where it falls short in protecting Aboriginal rights and how such shortcomings impact people’s lives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".