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Framing the Land: Canadian Landscapes Revisited in Jin-me Yoon and Lorraine Gilbert’s Photography

2023· article· en· W4389575412 on OpenAlexaboutno aff
Gwendolyne Cressman

Bibliographic record

VenueSillages critiques · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicPhotography and Visual Culture
Canadian institutionsnot available
Fundersnot available
KeywordsFraming (construction)PhotographyGeographyVisual artsArtArt historyArchaeology

Abstract

fetched live from OpenAlex

The representation of landscapes through survey photography in the mid to late 1800s largely contributed to document Canadian and American expansionist endeavors, to ascertain the documentary and scientific role that photography would play in that expansion and to foster a sense of national belonging and of Anglo-Saxon supremacy which would also find its expression in landscape painting. While the photographers engaged in geographical and topographical expeditionary missions envisioned the land as the epitome of the sublime landscape, the Group of Seven painters of the 1920s and 1930s later sought to express the essence of Canada’s northern identity through the celebration of a mythical wilderness. This manner of framing the land implied that what was kept outside of the frame or conversely included within its bounds, was often informed by hierarchical relations and colonialist visions of the land. I will be looking at the ways in which two contemporary Canadian photographers, Jin-me Yoon and Lorraine Gilbert, have revisited earlier landscapes in their works and how, through various processes of reframing, they reveal and contest the political, social and ecological underpinnings active in the production of these spaces. I will be paying attention to the critical visual investigation they deploy around Nordic landscapes defined not as the abstract spaces of the nation but as the places where issues of identity and memory unfold.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.924

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 studies0.0000.000
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 head0.021
GPT teacher head0.258
Teacher spread0.237 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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