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Record W4381157919 · doi:10.1111/nup.12452

Mattering: Per/forming nursing philosophy in the Chthulucene

2023· article· en· W4381157919 on OpenAlexaffabout
Annie‐Claude Laurin, Jane Hopkins‐Walsh, Brandon Blaine Brown, Patrick Martin, Emmanuel Christian Tedjasukmana

Bibliographic record

VenueNursing Philosophy · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPosthumanist Ethics and Activism
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPosthumanismPosthumanPerformativityMaterialismSociologyEpistemologyBiopowerSituatedFeminismCritical theoryGender studiesPhilosophyPoliticsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This paper presents an overview of the process of entanglement at the 25th International Philosophy of Nursing Conference (IPNC) at University of California at Irvine held on August 18, 2022. Representing collective work from the US, Canada, UK and Germany, our panel entitled 'What can critical posthuman philosophies do for nursing?' examined critical posthumanism and its operations and potential in nursing. Critical posthumanism offers an antifascist, feminist, material, affective, and ecologically entangled approach to nursing and healthcare. Rather than focusing on the arguments of each of the three distinct but interrelated panel presentation pieces, this paper instead focuses on process and performance (per/formance) and performativity as relational, connected and situated, with connections to nursing philosophy. Building upon critical feminist and new materialist philosophies, we describe intra-activity and performativity as ways to dehierarchise knowledge making practices within traditional academic conference spaces. Creating critical cartographies of thinking and being are actions of possibility for building more just and equitable futures for nursing, nurses, and those they accompany-including all humans, nonhumans, and more than human matter.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.841
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.101
GPT teacher head0.375
Teacher spread0.275 · 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.

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

Citations5
Published2023
Admission routes2
Has abstractyes

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