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Record W6961059316 · doi:10.14288/1.0406570

Dam to delta : visualizing landscapes of decarbonization in the Saaghii Naachii/Peace River region, Canada

2022· article· en· W6961059316 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsNexus (standard)IndigenousScholarshipResource (disambiguation)Human geographyClimate changeCorporate governanceAnthropocene

Abstract

fetched live from OpenAlex

This dissertation analyzes how decarbonization in Canada is mutually co-constitutive with processes of landscape change. Drawing from interdisciplinary scholarship in human geography and landscape architecture, with a focus on digital technologies of landscape visualization and manipulation, this dissertation critically analyzes how contemporary decarbonization agendas (re)produce conditions of uneven development and socioecological instability. Chapter 2 explores how the historical formation of energy landscapes in Canada’s boreal region continues to influence design proposals for the low-carbon transition, often at the expense of Indigenous communities and fragile ecologies. Chapter 3 calls attention to the pervasive tendency to depict human impacts upon Earth though highly abstract and aestheticized visualizations; a deeply depoliticizing practice I term planetary voyeurism. Chapter 4 builds upon this critique through a meta-review of water-energy nexus visualizations: a resource governance framework that claims to comprehensively depict relational linkages between energy generation and water use, yet which often elides the spatial, temporal, and hydrosocial dimensions of landscape. Lastly, Chapter 5 investigates the potential to misread and depoliticize strategies of putative decarbonization which may not, in fact, be carbon neutral; particularly when the cumulative effects of broader landscape transformations are considered. Through these analyses, this dissertation advances a place-based approach to decarbonization grounded in three key arguments: 1) decarbonization is constituted by and through relational landscapes characterized by shifting social, political, and ecological interrelationships; 2) technology is a powerful instrument of landscape change, and innovative digital tools of landscape perception and visualization can mitigate (or reinforce) processes of spatial injustice through the production of low-carbon technonatures and socionatures; and 3) decarbonization presents an opportunity to decentre human beings in the design and construction of built environments. Taken together, these arguments seek to challenge conventional approaches to decarbonization, elucidate more just and inclusive energy transition pathways, and advance more-than-human approaches to the design of post-carbon futures.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.166
Teacher spread0.156 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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