MétaCan
Menu
Back to cohort
Record W2741202141 · doi:10.1080/23251042.2017.1349638

Evaluating the effects of living with contamination from the lens of trauma: a case study of fracking development in Alberta, Canada

2017· article· en· W2741202141 on OpenAlexaffabout
Debra J. Davidson

Bibliographic record

VenueEnvironmental Sociology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLivelihoodHydraulic fracturingEnvironmental planningGeographyPolitical scienceEngineeringAgriculture

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.243
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations37
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental SociologySame topicAtmospheric and Environmental Gas DynamicsFrench-language works237,207