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Record W2594430832 · doi:10.1057/978-1-137-58641-4_4

Reassembling the Natural and Social Commons

2017· book-chapter· en· W2594430832 on OpenAlexaff
Jesse Bazzul, Sara Tolbert

Bibliographic record

VenuePalgrave Macmillan US eBooks · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPosthumanist Ethics and Activism
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsBiopowerMultitudeEnvironmental ethicsNatural (archaeology)EmpireCommonsPoliticsAssemblage (archaeology)SociologyPolitical scienceSocial scienceHistoryPhilosophyArchaeologyLaw

Abstract

fetched live from OpenAlex

Since modern times, human activity has significantly altered the earth’s physical and biological composition to such an extent that scientists have now renamed our current time the Anthropocene (Lewis and Maslin, Nature 519:171–180, 2015). Such a designation exposes the arbitrary boundary between the natural and social world. Large-scale social phenomena and injustices such as deforestation, mass agriculture, slavery, and brutal conquests now permanently mark planet Earth’s living and non-living components. Such events are enacted within assemblages of material and discursive components that have a history and ‘life’ of their own. This essay argues for dissolving the boundaries between ‘natural’ and ‘social’, human and non-human, and discursive and material, so as to fuse the natural and social commons (Hardt and Negri, Multitude: War and Democracy in the Age of Empire [Penguin, 2004]; Commonwealth [Harvard University Press, 2009])—the purpose of which is to create critical, activist spaces in education, integral to the exercising of biopower via the (re)production of subjectivities. Using Deleuze and Guattari’s assemblage theory and a short series of diagrams, we attempt to provoke reconfigurations of the material and social world.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.053
Scholarly communication0.0060.011
Open science0.0010.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.050
GPT teacher head0.311
Teacher spread0.261 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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