Bibliographic record
Abstract
The rapidly expanding forest bioenergy sector in Canada promises to support low carbon energy options that also support economic development and Indigenous involvement. Little empirical research has been conducted on Indigenous participation in forest bioenergy in Canada, which points to the need for a nuanced and reliable knowledge base to foster innovation in bioenergy that will contribute to community and economic development. However, before empirical research can be conducted it is important to understand the issues that influence Indigenous participation in the bioenergy sector. We therefore look to and conduct a frame analysis of allied sectors to develop insights about the policy and participatory landscape in which forest bioenergy in Canada is situated. Our analysis illustrates that identities and perspectives linked to energy and forestry can be complex and can shift depending on how business is done around such projects. Strengths in the current state of knowledge include the breadth of research regarding participatory natural resource management in Canada, particularly with regard to northern and Indigenous communities and territorial lands. Our review indicates that even the emerging bioenergy literature that exists now, when paired with that of allied sectors, can help analysts understand and make sense of energy and energy-related issues.
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.025 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".