Growing deep roots : learning from the Essipit's culturally adapted model of Aboriginal forestry
Bibliographic record
Abstract
Aboriginal peoples are seeking sustainable ways to steward and develop forests. Sustainable forestry is central to Aboriginal life and culture. Research indicates that the industrial forestry model has failed to address their socio-economic needs. To date, Aboriginal involvement in forestry is characterized by a limited economic role in forest development, limited influence over forest management, and an inability to integrate Aboriginal culture and values. The case study of Essipit (Quebec, Canada) provides new insight on how Aboriginal communities can contribute to sustainable forestry. Growing deep roots means using a culturally adapted model of forestry that is consistent with Aboriginal culture and values, which is therefore more likely to support long-term social change and economic growth. To ensure reliability and validity, this research employed four data gathering techniques: observation, documentation, interviews and focus-groups. Results identify the entrepreneurship framework that led to the success of Aboriginal forest enterprises in Essipit, the level of authority held by Essipit over forest governance, and Essipit objectives for forest-based development. Therefore, this thesis provides a framework that aims to support Aboriginal forest development in theory and practice. Despite constraints, such as timber access, capacity and institutions, Essipit was successful in engaging in forestry. Acquiring exclusive commercial rights to harvest wildlife became a key strategy that allowed Essipit to address social needs and create leverage for future forest-based activities. Essipit innovated in forest governance: they created a partnership with the forest company Boisaco and, thus, gained authority over forest management decisions at the operational level. Results indicate that the profitability motives of the forest industry are iii insufficient, because Essipit has other objectives and priorities. The forest industry looks primarily at the tree, while Essipit looks at everything that surrounds and supports it. This research emphasizes the importance of developing a model that will outlast changes in government or industry. A forestry model that has deep roots is integrated into the community and the culture. It can sustain these types of changes and keep growing. Without this understanding of Aboriginal experiences, knowledge and objectives, local initiatives and government policies will remain uninformed and, potentially, fail.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".