Bibliographic record
Abstract
Sherry: Could you please describe some of the projects your community has completed recently?Our focus is on relationships and how they have been important to the success of those projects.Vaughn: I'd start by saying that we have relationships with other First Nations, and we've been mentored when we were working on a project.We would go to other First Nations for advice.For example, when we did an industrial building, I visited Tom Maness, who is one of the best.They have one of the best industrial parks in Canada.I went and spoke to Tom and had a tour of Tom's facility.We have a partnership where we provide documentation and information on projects to other First Nations.When we built the Peace Tree Mall, I went to Six Nations, which already had a mini mall, and looked at their leasing, their construction, their design.We then altered it to something more suitable for us.We have relationships with other First Nations with an open door policy whereby people give us information and we share information with other First Nations on projects.We also have relationships with the government, both federal and provincial.Because of our geographic location, we run an entrepreneur program that receives support both federally and provincially.We have a really nice entrepreneur course, and if grants are available, we can work with various players in order to make a business successful.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".