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Record W7027630412

A Crude Case: Landscapes of Extraction in Canadian Contemporary Visual Culture

2017· dissertation· en· W7027630412 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2017
Typedissertation
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsnot available
Fundersnot available
KeywordsVisual cultureWildernessContext (archaeology)Meaning (existential)Environmental justiceSocial environmentVisual researchEconomic JusticeIsolation (microbiology)Visual rhetoric
DOInot available

Abstract

fetched live from OpenAlex

In this dissertation, I examine the relationship between visual culture and the culture of oil in Canada, specifically, the visual culture of the Alberta oil sands. I situate my research in the context of recent contentious environmental and infrastructural developments, including the Kinder Morgan pipeline (2004) and Northern Gateway pipeline (2006) proposals, and land claim disputes between the Athabasca Chipewyan First Nation and Canadian provincial and federal governments, which since the early-twenty-first century have been at the forefront of public discussions. This study seeks to explore what visual knowledge does, how environmental and social concerns are communicated visually, and the affective dimensions of these visualizations. I argue that the visual culture of the Alberta oil sands is both a strategic tool and a site of understanding that is key for advancing environmental and social knowledge. I examine the concept of wilderness in Canada and environmental and social justice as they are visualized in three areas: contemporary art, mass media and tourism, and activism. Each area forms a case study and is analyzed in context to explore the different ways in which the visual operates to deepen the moral and ethical dimensions of content—whether on informational, argumentative, emotional, or affective levels. Key to this discussion is sensitivity to the systems of meaning and value at play in each area, and how these both shape art, media, tourism, and activism and inform their reception. Attending to these systems of meaning also enables me to tease out within each area competing understandings, agendas, and ideologies, as well as relationships of one to another.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.088
Threshold uncertainty score0.635

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0390.036
Scholarly communication0.0150.004
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.000

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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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