The (In)Ability to Develop Indigenous Protected and Conserved Areas in Canada: A Literature Review
Bibliographic record
Abstract
The Canadian federal government’s latest conservation plan is hoping to achieve protected area targets of 25% of the land and water by 2025 and then increasing to 30% by the year 2030. The federal government also intended to move forward with reconciliation efforts and put into practice the Truth and Reconciliation Commission’s Calls to Action and the United Nations Declaration on the Rights of Indigenous Peoples. Conservation targets will only be possible with Indigenous support and involvement and therefore in 2018, the term Indigenous Protected and Conserved Area (IPCA) was created to move towards a conservation model, which included Indigenous Peoples’ values and traditions. Unfortunately, some groups believe this process might potentially be a double-edged sword, because this places a colonial framework of conservation on Indigenous land, which could be perceived as colonial entanglement instead of an act of reconciliation. Indigenous efforts to conserve or protect ecosystems in Canada are lengthy processes and the purpose of this research was to synthesize written resources to gain a better understanding of what it means to develop and designate IPCAs as well as some common challenges. The research involved a systematic literature review of Canadian supportive documents and was complimented by one-on-one semi-structured interviews with four practitioners. These methods were performed to gain insight on the written resources and education tools used when creating an IPCA in Canada. Five key themes were generated from assessing 148 documents, namely governance, habitat, cultural and spiritual values, sustainable economies, and boundaries that were all highly interconnected with one another. Key results of the research concluded that the ability or inability of the development of a successful IPCA and its designation was the result of collaboration efforts between Indigenous communities, industry, and government(s). The literature suggests it is possible to achieve effective collaboration between parties through the framework of “Ethical Space” or using a “Two-Eye Seeing” approach.
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.022 | 0.046 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".