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Record W4284993676 · doi:10.5038/1911-9933.16.1.1867

Climate in Crisis: Art and Activism at the Brooklyn Museum

2022· article· en· W4284993676 on OpenAlexvenueno aff
Nancy B. Rosoff

Bibliographic record

VenueGenocide Studies and Prevention · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicConservation Techniques and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousExhibitionExpansiveColonialismClimate changeGlobal warmingHistoryEnvironmental ethicsGeographyGenocideEthnologyPolitical scienceArchaeologyEcologyLaw

Abstract

fetched live from OpenAlex

This paper explores the Brooklyn Museum’s activism-centered museum practice as exemplified by the exhibition Climate in Crisis: Environmental Change in the Indigenous Americas. The exhibition presents the collections of Indigenous art from North, Central, and South America through the lens of climate change and its impact on the survival of Indigenous people. The main thesis is that the current climate emergency is part of a longer history of environmental colonialism that began five hundred years ago. For millennia, Indigenous communities throughout the Americas have maintained profound and expansive relationships with the natural world. However, beginning in the 1500s, Europe’s conquest and colonization of the Americas forced ways of using natural resources that clashed with traditional Indigenous modes of relating to the world. This fundamental difference in worldview—between one that sees human beings, animals, plants, and the land as interrelated and co-equal, and another that privileges human needs above everything else—has resulted in ever-escalating threats to Indigenous homelands, ways of life, and survival, as well as the unprecedented level of climate change affecting the planet today.

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0310.015
Scholarly communication0.0090.004
Open science0.0020.014
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0360.001

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 head0.056
GPT teacher head0.288
Teacher spread0.233 · 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
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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