Aboriginal Relations Guiding Principles and Guidelines
Bibliographic record
Abstract
Abstract In Canada, 1.4 million people self-identify as Aboriginal, distinctively represented as First Nations, Inuit and Metis. This total comprises 52 cultural groups, 11 major linguistic families and more than 50 distinct indigenous languages. The company’s oil and gas production operations and major projects are situated in western and Northern Canada in regions that have an Aboriginal population ranging from 6% to 51% of the total population. With a high density of Aboriginal peoples living in and around our areas of interest, the company recognizes the importance of meaningful engagement to ensure Aboriginal rights and title are respected, relationship risk is managed and the company’s social licence to operate is maintained. Given these business and social responsibility imperatives, the company created its "Aboriginal Relations Guiding Principles and Guidelines" (GP&G) in 2008. Consisting of five principles reinforced by guidelines in four focus areas, the GP&G represent the company’s formal commitment to how it intends to engage Aboriginal peoples, are a public demonstration of the importance it attaches to Aboriginal relations and are the foundation for supporting plans and programs.
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 | 0.037 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".