Biodiversity conservation policy reform and reconciliation in Canada: an analysis of the pathway to Canada target 1 through the policy cycle model
Bibliographic record
Abstract
In this article, we conduct an analysis of the Pathway to Canada Target 1 biodiversity conservation policy process to determine its level of inclusivity towards Indigenous Peoples and their knowledge systems. Also known simply as the Pathway, the policy focuses on Target 1 of Canada’s efforts to meet Aichi Target 11 of the Convention on Biological Diversity by 2020. The study aims to showcase the importance and meaningfulness of Indigenous involvement in the policy process. Simply including Indigenous actors does not automatically mean that their knowledge contributions to the policy were considered. Knowing why, when, and how Indigenous Peoples were engaged in the policy process helps us to see the role their presence and contributions played in co-producing policy knowledge for informing the Pathway to Canada Target 1 policy process. This is fundamental in reconciliation and in the improvement of conservation policies. After a review of the history and structure of the Pathway, paying attention to the importance of building relationship with Indigenous Peoples early in the policy process, we use the policy cycle model, outlining five stages of the policy process, to enable our analysis. While we have chosen the policy cycle model as a general framework for analyzing the stages of the policy process, it is a Western model, which falls short in its ability to reflect Indigenous worldviews adequately. Its use reveals, however, the degree of Indigenous engagement in each of the stages, demonstrating that the Pathway to Canada Target 1 did engage Indigenous Peoples at certain stages, in ways potentially reflective of what the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) and the Truth and Reconciliation Commission of Canada (TRC) Calls to Action demand. We conclude with recommendations for more collaborative governance in policymaking that would be more attentive to including Indigenous Peoples and their knowledge systems at all stages of the policy cycle.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".