Considering Cumulative Social Effects of Technological Hazards and Disasters
Bibliographic record
Abstract
This article describes research designs utilized to study cumulative sociocultural and psychosocial effects of technological hazards and disasters. We apply these designs to two cases: (a) the Exxon Valdez disaster with a focus on Cordova, Alaska, and (b) the Enbridge Northern Gateway Pipeline project with a focus on the Gitga’at First Nation in Hartley Bay, British Columbia, Canada. The Exxon Valdez oil spill began in 1989 with the grounding of the supertanker on Bligh Reef in Prince William Sound, Alaska. Fisheries collapsed, key species failed to recover, and litigation languished for 19 years, creating an accumulation of impacts from the initial event. The Gitga’at First Nation serves as a case for examining cumulative effects of energy development, specifically the Enbridge Northern Gateway Pipeline project proposed in 2010. Hartley Bay’s sociocultural and psychosocial well-being are under threat from these and other ongoing development activities; they have also endured centuries of government-led subjugation. In studying each of these communities, we used mixed methods approaches that combined document review, observations, interviews, and surveys. Based on our experiences, we contend that the most effective way to examine cumulative social impacts is to employ concepts and theories drawn from existing research to support guidelines, frameworks, and methods.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".