Developing and commercializing non-timber forest products: an Anishinaabe perspective from Pikangikum First Nation, Northwestern Ontario
Bibliographic record
Abstract
The purpose of this research was to understand an indigenous perspective on the development and commercialization of non-timber forest products, such as medicines and foods, in Pikangikum First Nation, Northwestern Ontario, Canada. Framed by a research agreement between Pikangikum First Nation and the University of Manitoba, this collaborative research was based on participant observation, field trips, semi-structured interviews, and community workshops. The appropriate development and commercialization of Anishinaabe mushkeekeeh (medicine) and meecheem (food) requires the guidance of community Elders, Anishinaabe knowledge, and traditional teachings. The community is cautiously interested in developing collaborative, diligent, and culturally respectful partnerships that interface knowledge systems. Benefit sharing means the joint ownership of intellectual property and financial benefits, developing employment and capacity-building opportunities for community members, and planning products for community use. This thesis offers a community perspective on how NTFPs might be researched, developed and commercialized in joint and mutually beneficial partnerships with a First Nation.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".