Aboriginal people and forestry companies in Canada: possibilities and pitfalls of an informal ‘social licence’ in a contested environment: Table 1
Bibliographic record
Abstract
Industrial forestry in Canada commonly occurs on the traditional territory of Aboriginal people, and forestry companies often take actions to gain acceptance and approval of communities. Over the last two decades, the concept of ‘social licence to operate’ (SLO) has been increasingly used as a way of framing these actions in relation to regulatory licences and approval processes. Although Aboriginal views of forestry have been extensively researched, few attempts have been made to link this research to the developing concept of SLO. This article seeks to address this gap, using existing models to identify five groups of path elements that contribute to obtaining and maintaining SLO: socio-economic infrastructure, biophysical infrastructure, engagement processes, relationship building and recognition of rights. Previous research on five common forms of collaborative arrangement – impact benefit agreements, co-management, consultation processes, tenures and economic partnerships – is then reviewed to consider how these contribute to obtaining and maintain SLO. Canadian experiences demonstrate the potential benefits of direct negotiations and the advantages of combining arrangements, but also highlight the difficulty of addressing Aboriginal rights within an SLO framework.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.028 | 0.011 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".