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Growing (with) Muskeg: Oil Sands Reclamation and Healing in Northern Alberta

2021· article· en· W3160839084 on OpenAlexaffvenueabout
Tara L. Joly

Bibliographic record

VenueAnthropologica · 2021
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsLand reclamationBureaucracyIndigenousOil sandsBorealGovernment (linguistics)GeographyLand useEnvironmental resource managementArchaeologyEcologyEnvironmental sciencePolitical scienceCivil engineeringEngineeringLawPolitics

Abstract

fetched live from OpenAlex

Scientists working for oil companies in the Athabasca region are developing methods by which to reclaim muskeg (boreal peatlands) on land disturbed by oil sands extraction. The Alberta government requires companies to reclaim disturbed land by achieving equivalent capability of the landscape to support an end land use. Indigenous community members instead define reclamation as establishing not only quantifiable ecological functions, but also relationships to their traditional territories. Tensions emerge as Indigenous concerns are often subsumed within bureaucratic discourses that favour scientific classification and quantification of land uses in reclaimed areas. Divergent responses to muskeg in reclamation activities are informed in part by these competing emphases on quantifiable landscapes as opposed to those that are relational and growing. This article traces this multiplicity through the examination of government and scientific literature and ethnographic fieldwork with Indigenous communities in northern Alberta. Muskeg is used as an analytical tool to explore competing conceptions of land reclamation. Mistranslation of polysemantic terms like muskeg occur on an ontological level, and settler colonial relations and power imbalances between competing languages and knowledge systems proliferate in reclamation activities.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.997

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 studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.370
Teacher spread0.333 · 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 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

Citations4
Published2021
Admission routes3
Has abstractyes

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