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Record W4389967363 · doi:10.14453/asj/v12i2.9

Rhetorics of Species Revivalism and Biotechnology – A Roundtable Dialogue

2023· article· en· W4389967363 on OpenAlexaff
Eva Kasprzycka, Charlotte Wrigley, Adam Searle, Richard Twine

Bibliographic record

VenueAnimal studies journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAnthropoceneFutures contractEnvironmental ethicsConversationCorporate governanceSociologyExtinction (optical mineralogy)RestructuringPolitical scienceEnvironmental planningBiologyBusinessLawGeographyManagementEconomics

Abstract

fetched live from OpenAlex

This informal dialogue contextualises and explores contemporary practices of nonhuman animal gene-modification in de-extinction projects. Looking at recent developments in biotechnology’s role in de-extinction sciences and industries, these interdisciplinary scholars scrutinise the neoliberal impetus driving ‘species revivalism’ in the wake of the Capitalocene. Critical examinations of species integrity, cryo-preservation, techno-optimism, rewilding initiatives and projects aimed at restoring extinct animals such as the woolly mammoth and bucardo are used to map some of the necessary restructuring of conservation policies and enterprises that could secure viably sustainable – and just – futures for nonhuman animals at risk of extinction. The authors question what alternatives are being ignored in the wake of technoscientific responses to the climate emergency, and interpret the motivations, tactics and tools responsible for commodifying nonhuman animals down to the cellular level. Our conversation on the messy relations within endangered ecologies offers alternative approaches to environmental governance and strategies for addressing the climate and biodiversity crises 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 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)0.001
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 score0.577
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.125
GPT teacher head0.385
Teacher spread0.260 · 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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