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Record W2310946183 · doi:10.1177/0263775815615123

Fish-hood: Environmental assessment, critical Indigenous studies, and posthumanism at Fish Lake (Teztan Biny), Tsilhqot’in territory

2016· article· en· W2310946183 on OpenAlexaffabout
Dawn Hoogeveen

Bibliographic record

VenueEnvironment and Planning D Society and Space · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFish <Actinopterygii>FisheryEnvironmental impact assessmentFish stockIndigenousGeographyEcologyPolitical scienceLawBiology

Abstract

fetched live from OpenAlex

This article is about how fish are regulated through environmental assessment. The theoretical frame used is nested between critical Indigenous studies and posthumanism which allows for an interrogation of how fish are very formally produced as disposable through mining regulatory procedures, such as environmental impact statements, that form the basis of the environmental assessment processes in Canada. The site of analysis is Fish Lake on Tsilhqot’in lands in the interior of British Columbia. I look at the corporate-scientific foundation of Canadian federal environmental assessment and an amendment within the federal Fisheries Act that allows fish bearing lakes to be turned into mining waste areas. An analysis of the use of these regulations illuminates part of the story about a twice-rejected copper–gold mine proposal at Fish Lake. In light of how Canadian regulations can produce fish as disposable, I explore if the value of fish can be explained externally from capital accumulation and technical representations in scientific studies within environmental assessment. The article reveals the environmental assessment process largely negates what I refer to as “fish-hood.”

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score0.997

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 studies0.0040.001
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 head0.017
GPT teacher head0.295
Teacher spread0.277 · 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.

Study designQualitative
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

Citations49
Published2016
Admission routes2
Has abstractyes

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