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Record W6999070406

Climate Crisis in the Shadows : Rethinking Our Relationships with Nocturnal Kin

2023· other· en· W6999070406 on OpenAlexaboutno aff

Bibliographic record

VenueLancaster EPrints (Lancaster University) · 2023
Typeother
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeCrepuscularCreaturesNocturnalSubject (documents)DisgustExtinction (optical mineralogy)Vulnerability (computing)Natural (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

For many people the impacts of climate change are subject to a high degree of speciesism, with the wider nonhuman suffering remaining largely unseen in public consciousness. This is arguably even more profound when we think about climate change and its impacts after dark (Cox et al. 2020). When we consider our relationships with other species, we typically bring to mind our daytime experiences and, thus, the nonhumans that we might encounter or anticipate being active. However, the vast majority of nonhumans are crepuscular or nocturnal, going about their lives out of sight and out of mind of most people. This paper, therefore, seeks to address this gap in knowledge and understanding by questioning our relationships with nonhuman life after dark. Specifically, we seek to embrace one of the most misunderstood nocturnal creatures, the rat. Rats are taboo, they are mythical creatures that transgress the boundaries of our real world and the imaginary. Of particular concern for us is how quickly they are being impacted by the climate crisis. One way to shift the attention to the nonhuman, such as rats, is to consider what is currently being lost, and to view our ecological state as the sixth mass extinction (Morton 2021). Specifically, in 2016, a rat-like creature Bramble Cay Melomys, was the first mammal recorded to become extinct as a result of anthropogenic climate change (Panagiotarakou 2020). Although rats are often depicted as our near neighbours, they are seldom perceived as kin. Rats are “animals that disgust us” (Jerolmack 2008), and conjure forth notions of danger and disease. What’s worse, there are ways in which rats are not even considered animals, as they are excluded from the Animal Welfare Act and thus lack any legal protection in the US (Smith 2002). Our project suggests that if we can establish kinship with rats, then barriers to wider nocturnal multispecies companionship could be profoundly unlocked. This emerging practice-based project develops speculative-performative interventions for and with real and imaginary rats. The authors will create scenes of “fictional activism” (Williams Gamaker 2021), drawing from the Medieval legend of the Pied Piper of Hamelin. The legend is considered a multi-layered and early version of nonhuman displacement engineered by humans, and stigmatizing another species. These site-specific interventions, accompanied by customarily composed science-fiction pipe songs and poems, integrate climate science evidence from the rats’ perspective as predictions of their past, present, and future trouble. The songs are, on the one hand, composed to expand the rats’ intellectual capacities, and hence their chances of survival. Namely, controversial test results suggest improved maze learning after exposing rats to music, also known as the Mozart effect (Rauscher et al. 1998, Steele 2003). On the other hand, the songs and poems aim to decolonize human attention from consumerism (Halifax 2021) and human-centricism, and to rewild our hearts to develop multispecies compassion (Bekoff 2014). The project thus aims to change the perception of rats as a public health hazard and (black) “death”, into creative but endangered species capable of experiencing suffering.

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)0.005
metaresearch head score (Gemma)0.009
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.013
Scholarly communication0.0070.011
Open science0.0020.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.059
GPT teacher head0.288
Teacher spread0.228 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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