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Record W4315784175 · doi:10.3389/fsoc.2022.1056277

Imposing calculations: The visibility and invisibility of harm in the Mackenzie Gas Project environmental assessment

2023· article· en· W4315784175 on OpenAlexafffund
Carly Dokis

Bibliographic record

VenueFrontiers in Sociology · 2023
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsNipissing University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHarmInvisibilityIndigenousEnvironmental ethicsScope (computer science)EthnographySociologyWork (physics)Settlement (finance)Environmental impact assessmentPolitical scienceLawEcologyEconomicsEngineeringAnthropology

Abstract

fetched live from OpenAlex

Environmental assessment is an institutional apparatus through which proponents concede harm associated with extractive projects. Within these processes proponents define the nature and scope of harm, which is made visible through the production of indicators and measurements and made manageable through mitigation measures or economic compensation. That the activities of extractive industries may have effects on surrounding ecologies is rarely in question; proponents of extractive projects regularly concede that their activities will result in negative (but also positive) changes to environments and communities. What is often contested in the course of environmental assessment and regulatory processes is the "significance" of the impacts identified, the nature of the harm caused, and whether or not it is possible or acceptable to accommodate it. Drawing from ethnographic fieldwork conducted in the Sahtu Settlement Area, NWT during the Mackenzie Gas Project environmental assessment, along with regulatory documents and transcripts, this paper examines how proponents and regulatory regimes work to make the impacts of extractive industries visible, and how these logics deviate discursively and materially from many Indigenous peoples' understandings of appropriate relationships between human beings and nature.

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)0.001
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 score0.112
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.276
Teacher spread0.259 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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