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Record W3016374488 · doi:10.1017/hyp.2020.7

Nature's Relations: Ontology, Vulnerability, Agency

2020· article· en· W3016374488 on OpenAlexaff
Didier Zúñiga

Bibliographic record

VenueHypatia · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAgency (philosophy)Environmental ethicsPoliticsVulnerability (computing)EpistemologyMaterialismOntologyTechneSociologyAnthropocentrismPolitical philosophyEngineering ethicsPolitical scienceSocial scienceLawPhilosophyComputer science

Abstract

fetched live from OpenAlex

Abstract Political theory and philosophy need to widen their view of the space in which what matters politically takes place, and I suggest that integrating the conditions of sustainability of all affected—that is, all participants in nature's relations—is a necessary first step in this direction. New materialists and posthumanists have challenged how nature and politics have traditionally been construed. While acknowledging the significance of their contributions, I critically examine the ethical and political implications of their ontological project. I focus particularly on how the decentering of human agency that they advocate for raises a set of concerns that need to be addressed in developing an appropriate ecological ethics. I argue that the latter must be attuned to the vulnerability of living beings who participate in relationships that sustain life on earth. This brings me to conclude that qualitative distinctions between the worlds ofbiosandtechneare necessary. This is because we need to think critically about ways of evaluating types of relationships so that we can assess them and establish which are worth nurturing and protecting and which are not.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0050.070
Scholarly communication0.0100.011
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.354
Teacher spread0.308 · 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 designTheoretical or conceptual
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
Published2020
Admission routes1
Has abstractyes

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