The role of federal guidelines in the Evolution of cumulative effects assessment research in the Canadian forest ecosystem
Bibliographic record
Abstract
Cumulative effects assessment (CEA), as a subset of environmental impact assessment, has been used over the past half-century to evaluate the impact of anthropogenic and natural processes on the integrity of forest ecosystems. In 2019, the Canadian federal government launched the Impact Assessment Act (IAA) and the Practitioner’s Guide to Federal Impact Assessment to replace the 2012 Canadian Environmental Assessment Act (CEAA) and the Practitioner’s Guide. The new act emphasizes, among other issues, the need for CEA research to actively engage Indigenous communities and the public and apply a gender-based analysis (GBA) plus framework in their assessment. This paper aims to identify how cumulative effects research in Canadian forestry has progressed over time and how it aligns with federal government guidelines under the Impact Assessment Act of 2019. Through a systematic review of CEA research in Canada since 1992 and a document analysis, we examine the conduct of CEA research and compare the 2012 CEAA and the 2019 IAA and their respective practitioners’ guides to identify key similarities and differences and establish the influence of the 2019 IAA on CEA research. This accounts for how adaptive CEA research in Canada is to the changing landscape, specifically in measuring and addressing the impacts of disturbances on different interest groups. While much of CEA research followed the impact identification and reporting requirements of the 2012 CEAA and 2019 IAA, few research projects, particularly those written after 2019, paid much attention to Indigenous and public participation, while none considered GBA Plus in their analysis. This is despite the availability of GBA Plus, Indigenous, and public engagement guidelines and resources identified in the 2019 practitioners guide. We recommend active engagement between federal regulatory agencies and industry to ensure that industry and researchers have the proper training and resources to facilitate more meaningful consideration and integration of GBA Plus, including Indigenous and public engagement processes in their CEA research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".