Expert opinion on the criteria and indicator process and Aboriginal communities: Are objectives being met?
Bibliographic record
Abstract
Developed in the 1990s, the process of criteria and indicators (C&I) has been used to conceptualize, evaluate and implement sustainable forest management (SFM). However, to assess their effectiveness we explore whether their use in management leads to changes, especially at the local level in Aboriginal communities. More specifically, can C&I justify Aboriginal use of C&I? Since local-level C&I are a recent initiative, the effectiveness of the C&I process in assessing progress towards SFM was assessed via interviews with experts associated with the development of local-level Aboriginal C&I frameworks in Canada on use, integration and needs of Aboriginal communities for C&I. Our results suggest that C&I in Aboriginal communities are considered to be “just another reference point” because: 1) Aboriginal objectives are maintained at arm's length from the forest management process; 2) the use of C&I as a negotiating tool has not been sufficient to culturally adapt forest management for Aboriginal values and objectives and 3) Aboriginal values have been restricted to the elaboration of C&I and the Aboriginal definition of SFM, but they are not part of the evaluation nor the implementation of SFM. In contrast to the forest industry, Aboriginal communities identified the following objectives as motivation for using C&I: Aboriginal representation, Aboriginal engagement, capacity building and empowerment. Without explicitly acknowledging these Aboriginal community objectives, C&I becomes a tool restricted primarily to forest managers and thus sustainable forest management becomes unattainable. In effect, the underlying issue is not C&I in themselves but the limited role Aboriginal communities have been allowed to have in the SFM process.
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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 | 0.074 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".