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Record W4401632254 · doi:10.22215/etd/2024-16115

Stó:lō Relationalities: Exploring Infrastructures of Climate Adaptation along the Fraser River

2024· dissertation· en· W4401632254 on OpenAlexaboutno aff
Wilson Tian Zhi Jiang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousClimate changeColonialismGeographyClimate change adaptationEmbodied cognitionPoliticsFlooding (psychology)Adaptation (eye)SociologyEnvironmental ethicsPolitical scienceEcologyArchaeologyLaw

Abstract

fetched live from OpenAlex

Stó:lō, or the Fraser River in British Columbia, is a site of many intersecting forces, from the urgency of climate change caused by flooding and ecological degradation to the material and political hierarchies produced by settler-colonial infrastructures. All of which is imposed onto a rich landscape of indigenous life and history. This paper's approach follows three phases—encountering, entangling, and engaging—of indigenous culture from a non-indigenous (Chinese Canadian) perspective. The process analyzes, practices, and builds on connected histories of indigeneity, migrant labour, and modern colonial conditions. ‘Mapping’ also becomes integral for macroscopic and embodied interpretations of the Stó:lō, followed by the design of a socio-ecological infrastructure addressing climate change from a cultural angle. The paper concludes by reflecting on place-knowing for architectural practice, engagements with indigeneity, LANDBACK, and the cultural dimensions of climate change, suggesting further steps for related projects.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.574

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.012
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.071
GPT teacher head0.374
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2024
Admission routes1
Has abstractyes

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