Sowing the future: A better understanding of Corporate-Indigenous Community Economic Partnerships in the Québec forestry sector
Bibliographic record
Abstract
A major challenge for the forestry sector is providing Indigenous communities a fairer share of benefits resulting from forest-based development. This can be achieved by building Corporate-Indigenous Community Economic Partnerships (CICEPs). However, this avenue requires a better understanding of: 1) the structures that Corporate-Indigenous Community Economic Partnerships (CICEPs) can take and the impact of these structures on the relationship between both parties; and, 2) the necessary capitals for the creation of CICEPs so that they can meet the criteria of equitable sharing, while generating mutually profitable benefits. To address these knowledge gaps, we conducted a qualitative research involving 21 semi-structured interviews with Indigenous and industry representatives located in Québec, Canada. CICEPs can be divided into four structures: informal agreement, service contracting relationship, memorandum of understanding and joint venture. These partnerships can be translated into different forms of capital: human, social, political and financial. To promote and maximize CICEPs, it is necessary to link the structure-based approach with the capital-based approach. This research shows that more structured partnerships require larger investments in various forms of capital.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".