Radicalizing Care : Feminist and Queer Activism in Curating
Bibliographic record
Abstract
Essays, practice-informed case studies, and manifestos offer insights into what happens when feminist and queer care ethics are put into curating practice. \n 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. \n These texts examine a year-long program at Schwules Museum Berlin focused on the perspectives of women, lesbian, inter, non-binary and trans people at Schwules Museum Berlin; 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. \n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.090 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".