Evaluating the effects of living with contamination from the lens of trauma: a case study of fracking development in Alberta, Canada
Bibliographic record
Abstract
Trauma, the experience of sudden, dangerous, overwhelming events that render victims powerless, is an apt description of many experiences with toxic contamination. Toxic contamination events nonetheless often have a number of characteristics in common that render such events unique forms of trauma, including the invisibility and ambiguity of threats, an association between the threat and sources of livelihood and identity and the absence of resources necessary for resolution and recovery. While environmental sociologists tend not to analyze toxic contamination from the lens of trauma, doing so may shed important insights into such events and their human and social consequences. The current study explores the toxic contamination experienced by local residents due to nearby hydraulic fracturing activities in rural communities in southern Alberta, a conservative, upper middle class agrarian region with strong links with the oil and gas industry. Residents describe acute impacts to their health, land, livestock and loved ones, but these traumas were then exacerbated by the failure of authorities to respond in a manner expected, and the corrosion of communities. Victims experienced complete upheaval in their beliefs, and for many their experiences with contamination and fears of future exposure have come to dominate their lives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".