Toward a common categorization for valued components: using a review of valued components and indicators in the lower James Bay Region of Ontario and Quebec, Canada, to support cumulative impact science in Canada
Bibliographic record
Abstract
Cumulative impacts and regional assessments (RA) require the integration and analysis of large quantities of interdisciplinary scientific information. Yet, the information available is not often readily accessible nor standardized across impact assessments. We proposed here a categorization of valued components that have been derived from projects in the James Bay Lowlands and that is aligned with the IAAC’s Tailored Impact Statement Guidelines (TISG) template, a key document to support project and regional impact assessment deliverables. We compiled valued components and indicators from previous and ongoing environmental impact assessments in the James Bay Lowlands. We identified trends associated with valued components and indicators and combined these trends with the TISG. We then derived a categorization of valued components nested under seven systems. From the reviewed work, we identified 197 valued components (23% valued components were common for two or more projects) and 313 indicators. Our categorization is composed of seven systems and 34 potential valued components allowing for easy connections between future project-level impact assessments and RAs. Our categorization is a valuable communication tool across all stakeholders and rightsholders involved in impact assessments, including communities, Indigenous leaders, IA practitioners, industry, and government.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".