Painting from the Other Side: Tracing the Reparative Turn in Contemporary Practice
Bibliographic record
Abstract
Painting from the Other Side, is the curatorial project section of a larger interdisciplinary practice-led research project titled Embodying the Reparative Turn: Seeking Agency through Studio Practice in Individual and Collective Contexts that investigates the potential of the reparative turn in painting, aesthetics, narrative, and curation to subvert, evade, and exit from dynamics of exclusion linked to homophobia, misogyny, and racism. It considers how systemic cultural agents propagating exclusion deploy inequity to obstruct human flourishing, then explores how they are subverted through diverse reparative practices in painting. Painting from the Other Side included an open call for paintings that engage with reparative content by artists whose identities are in some way outside of the minority power position of the Western canon of painting by straight white male artists. It included intensive studio visits and culminated in an exhibition. This paper proposes a theoretical framework of reparative painting and practice, tracing the many paths research-participant artists followed towards a reparative turn in painting.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.059 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".