What do we mean when we talk about care?
Bibliographic record
Abstract
As part of the ‘Women in Revolt’ conference at Tate Britain, this out loud reading group responded to current debates on the need for art institutions to be more ‘accessible’ and ‘inclusive,’ touching on intersectional feminist explorations of care and access, collectivity and healing. Readings opened up questions about the different associations and political urgencies of terms including ‘care,’ ‘hospitality,’ ‘access’ and ‘access intimacy.’ Highlighting how art institutions have both co-opted and wilfully misconstrued practices of care, the session points to how we might care for one another better, while also pushing cultural institutions to take better care of us. The session was contextualised and introduced by Helena Reckitt and Beth Bramich of the Feminist Duration Reading Group. Discussions during the session highlighted the problematics of an event branded as care-oriented that met in a room without ventilation or PPE. Readings Together we read selections from: • Leah Lakshmi Piepzna-Samarasinha, from ‘Care Webs: Experiments in Creating Collective Access,’ in Care Work: Dreaming Disability Justice, Vancouver: Arsenal Pulp Press, 2018, pp41-47 introduced by guest artist Leah Clements • Johanna Hedva, Sick Woman Theory, 2020 (based on original essay in Mask Magazine, 2016) • Johanna Hedva, Why It’s Taking So Long, Topical Cream, 2022 introduced by Katrin Lock of the Feminist Duration Reading Group
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 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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.078 |
| Scholarly communication | 0.027 | 0.031 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.028 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".