Bibliographic record
Abstract
Artist Keisha Scarville spent her childhood raised in Brooklyn where her parents, along with so many other Guyanese immigrants, migrated and settled after leaving Guyana in the 1960s. In her photography essay, ‘Surrogate Skin: Portrait of Mother (Land),’ Scarville reflects on her portraiture series, ‘Mama’s Clothes,’ an homage to her late mother. In the portraits, Scarville embodies her mother’s dresses to evoke her connection to Guyana. In both her prose and portraits, Scarville grounds herself in her mother's place of birth of Buxton (Guyana) and her neighborhood of Flatbush (US). The lush, organic landscapes in these images, shifting between Guyana and the US, hold emotional and geographical significance: they capture the artist and her mother’s dance between transient spaces. Grappling with ‘a sense of displacement and an internal fracturing’ after her mother’s passing, Scarville looked to her ‘mama’s clothes’ of bright colors, strong prints, and long flowing fabrics for meaning. She drapes and layers her body in her mother’s clothing as well as fashions masks and veils to cover her face, which is always obscured. In merging her body with her mother’s clothes, Scarville marries both time and space—two generations, two homelands, and the complexities in between.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.098 | 0.031 |
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".