Reversing the gaze: understanding how community members are negatively affected by impact assessment
Bibliographic record
Abstract
This study addresses the persistent tensions in Impact Assessment (IA), which aim to balance public engagement with technical analysis. In this context, the IA process itself poses a significant risk to the individuals and communities involved in the impact assessment. The consequences of these risks, and whether they materialize, remain uncertain and depend on the specific context of each IA process. Drawing on the sociology of risk and broader IA literature, we present a case study of the Grassy Mountain Coal Project in southern Alberta, Canada. Instead of evaluating project-specific impacts within the IA process, we examine the impacts of the IA process on community members. By reversing our gaze, we aim to understand how IA processes impact communities and how these impacts manifest. Led by the results of the study, the impacts of the IA on community members are examined under three specific impact locations: (1) risk perceptions and anticipatory impacts, (2) procedural issues, and (3) community and regional conflicts. Our conclusions underscore the need to recognize these types of impact on communities and explore ways to minimize the costs of IA to communities.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".