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Record W4235690532 · doi:10.1515/energyo.0049.00002

Climate Change Theater and Cultural Mobility in the Arctic: Chantal Bilodeau’s Sila (2014)

2019· dataset· en· W4235690532 on OpenAlexaboutno aff
Nassim W. Balestrini

Bibliographic record

Venueenergyo · 2019
Typedataset
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsArcticThe arcticClimate changeOceanographyGeographyPhysical geographyGeology

Abstract

fetched live from OpenAlex

Based on an interdisciplinary approach to mobility, this paper scrutinizes how Chantal Bilodeau's climate change play Sila (which had its professional premiere in 2014) unravels mechanisms of meaning construction that prevail in discourse on climate change in the Arctic. Bilodeau addresses the impact of climate change on the Arctic by foregrounding competing notions and practices of mobility promoted by various interest groups living or working in Arctic Canada. Through a focus on real and figurative references to breath and to lines/paths, the play highlights individual experiences of loss and mourning rather than specific instances of environmental disaster. Sila demonstrates that the characters' socioeconomic status, ethnicity, gender, generation, and species – along with their attitudes towards human and non-human, material and spiritual realms – largely determine which kinds of mobility they are associated with and how these types of physical or cultural mobility are assessed. The play responds to the challenge of dramatizing climate change, a phenomenon which is not graspable through single spectacular environmental disasters, by immersing theatergoers into a complex aestheticized network of life-giving and life-depriving natural and human-made situations and narrative practices that demonstrate intersections between local and global, synchronic and diachronic, material and immaterial phenomena.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.104
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.072
GPT teacher head0.385
Teacher spread0.313 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2019
Admission routes1
Has abstractyes

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