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Record W2944981723

Bat City: Becoming with Bats in the Austin Music Scene

2018· article· en· W2944981723 on OpenAlexvenueno aff
Julianne Graper

Bibliographic record

VenueMUSICultures · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeColonialismMusicalNatural (archaeology)SociologyAestheticsHistoryAnthropologyArtVisual artsLiteratureArchaeology
DOInot available

Abstract

fetched live from OpenAlex

In the 1990s, Bruno Latour contested the idea that modern societies are defined by their separation from the natural world. In this essay, I offer a case study from Austin, TX, examining how human-bat relationships have blurred the lines between the natural and the cultural in a process that I term “becoming with,” following Donna Haraway. I begin by discussing negative stereotypes about bats drawn from both colonial history and anti- immigrant narratives. I then explore the development of Austin into the “Bat City,” a process which radically revised these colonial preconceptions. Finally, I explore a musical case study that exemplifies Austin's relationship to its local bat colony: horror-surf band the Bat City Surfers, who describe themselves as evolutionary descendants of bats.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0360.014
Scholarly communication0.0090.004
Open science0.0010.008
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.062
GPT teacher head0.330
Teacher spread0.268 · 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 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

Citations4
Published2018
Admission routes1
Has abstractyes

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