Selection of valued ecosystem components in cumulative effects assessment: lessons from Canadian road construction projects
Bibliographic record
Abstract
Valued ecosystem component (VEC) selection is a core component of cumulative effects assessment (CEA) and gives direction to impact analysis, mitigation and monitoring. Yet little is known about CEA VEC selection practices. This paper examines 11 Canadian road infrastructure project CEAs completed between 1995 and 2011 to determine how VEC selection in CEA is performed, and whether these practices are sensitive to the linear project development context. Document review and semi-structured interviews reveal an absence of VEC selection guidance, late timing of cumulative effects considerations in impact assessment, lack of sensitivity in CEA VEC selection to the unique, linear nature of the road construction projects and a general lack of insightful, creative approaches to CEA VEC selection – ones that better reflect potential impacts to social and economic aspects of the environment – despite it being shown to be a values-driven, subjective process. There is a clear need for regional databases to support consistent CEA VEC selection processes, and the development of CEA-specific VEC selection guidance.
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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".