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Record W6963884736 · doi:10.21937/9783956795909

Radicalizing Care - Feminist and Queer Activism in Curating

2022· article· en· W6963884736 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsQueerPoliticsFeminismImmigrationEthnographyHegemonyCultural studiesState (computer science)Aotearoa

Abstract

fetched live from OpenAlex

What happens when feminist and queer care ethics are put into curating practice? What happens when the notion of care based on the politics of relatedness, interdependence, reciprocity, and response-ability informs the practices of curating? Delivered through critical theoretical essays, practice-informed case studies, and manifestos, the essays in this book offer insights from diverse contexts and geographies. These texts examine a year-long program at the Schwules Museum Berlin focused on the perspectives of women, lesbian, inter, non-binary, and trans people at the Schwules Museum; the formation of the Queer Trans Intersex People of Colour Narratives Collective in Brighton; Métis Kitchen Table Talks, organized around indigenous knowledge practices in Canada; complex navigations of motherhood and censorship in China; the rethinking of institutions together with First Nations artists in Melbourne; the reanimation of collectivity in immigrant and diasporic contexts in welfare state spaces in Vienna and Stockholm; struggles against Japanese vagina censorship; and an imagined museum of care for Rojava. Strategies include cripping and decolonizing as well as emergent forms of digital caring labor, including curating, hacking, and organizing online drag parties for pandemic times. With contributions by Edna Bonhomme, Birgit Bosold, Imayna Caceres, Pêdra Costa, COVEN BERLIN, Nika Dubrovsky, Lena Fritsch, Vanessa Gravenor, Julia Hartmann, Hitomi Hasegawa, Vera Hofmann, Hana Janečková, k\are (Agnieszka Habraschka and Mia von Matt), Gilly Karjevsky, Elke Krasny, Chantal Küng, Sophie Lingg, Claudia Lomoschitz, Cathy Mattes, Elizaveta Mhaili, Jelena Micić, Carlota Mir, Fabio Otti, Ven Paldano, Nataša Petrešin- Bachelez, Nina Prader, Lesia Prokopenko, Patricia J. Reis, Elif Sarican, Rosario Talevi, Amelia Wallin, Verena Melgarejo Weinandt, Stefanie Wuschitz.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.245
Teacher spread0.226 · 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 designObservational
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

Citations2
Published2022
Admission routes1
Has abstractyes

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