History, engagement, and visibility of Indigenous Peoples in urban forest management plans from Canada and the United States
Bibliographic record
Abstract
Environmental justice in urban forestry is concerned with recognizing and centering the needs of under-engaged communities in planning and decision-making processes. To examine the engagement and visibility of Indigenous Peoples in urban forestry, this study reviewed 181 urban forest management plans (UFMPs) from Canada and the United States. Themes pertaining to Indigenous Peoples were found in only 16 Canadian (22 %) and 27 US (25 %) UFMPs. The most common reference to Indigeneity in the UFMPs briefly referred to the pre-European history of the region, often omitting how settler-colonialism impacted the socio-ecological landscape. Only three UFMPs included a land acknowledgement, despite all cities being located on traditional territory of Indigenous Peoples. Even with a focus on the socio-ecological benefits of urban trees throughout the UFMPs, few referenced socio-cultural and provisioning benefits for Indigenous Peoples specifically. On-going Indigenous engagement was only discussed in one UFMP, although five acknowledged Indigenous engagement in creating the UFMP and eight mentioned plans for long-term engagement. Reconciliation was described in three UFMPs, with one Canadian UFMP referencing the Truth and Reconciliation Commission’s Calls to Action. Given the deep cultural connection that Indigenous communities share with the environment, the impacts of settler colonialism, and the underrepresentation of reconciliation and decolonization principles with urban environmental planning, it is imperative that Indigenous Peoples be recognized and centered within urban forest planning and decision-making. Supporting engagement and co-governance with Indigenous Peoples acknowledges their rights, traditional ways of knowing, promotes recognitional justice, and helps to foster a more sustainable and just urban future.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".