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Record W4389967347 · doi:10.14453/asj/v12i2.11

Sites of Cultural Production in Response to Mass Extinction

2023· article· en· W4389967347 on OpenAlexaff
Stephanie S. Turner, EvaMarie Lindahl, Tara Nicholson

Bibliographic record

VenueAnimal studies journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStorytellingConversationSociologyAestheticsExtinction (optical mineralogy)KinshipEnvironmental ethicsScholarshipWitnessExtinction eventHistoryEpistemologyNarrativeAnthropologyLiteratureArtPolitical scienceCommunicationLawPhilosophyBiology

Abstract

fetched live from OpenAlex

This conversation, mediated by Tara Nicholson, considers Stephanie Turner and EvaMarie Lindahl’s research in cultural representations of extinction and investigations of more-than-human forms of storytelling through an art historical lens. In response to Lori Gruen’s classification, extinction is a distinctive loss of ‘animal cultures’. It is more than biodiversity destruction or a static inventory of a species’ death. Nonhuman ways of building bonds, reproducing, teaching offspring, constructing homes and mourning the dead, are all systems of knowledge lost in extinction (Gruen et al. 2017). This conversation offers compassionate ways of bearing witness to species destruction and a space for empathy and kinship. The authors ask, how can dialogue between science and art lead to new understandings of the ‘wicked problem’ of mass extinction during climate crisis? Examining methodologies of cross-disciplinary storytelling and cultural production, this exchange connects museum practice, large-scale public artworks and artistic research as types of embodied knowledge to promote public awareness surrounding the acceleration of species extinction.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.098
GPT teacher head0.412
Teacher spread0.314 · 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 designQualitative
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
Published2023
Admission routes1
Has abstractyes

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