Bibliographic record
Abstract
Whenever I’ve been asked about my abstract paintings, I’ve always found myself beginning with the process rather than the work itself. It is within the process—its tangibility, its motion—that the best clues emerge. Now I wonder: if these clues help others connect, could they also help me? Are they pieces of a larger puzzle I’m still solving? Am I chasing the painting—or the painter? My aim in this thesis is not to simply show my personal feelings, but to share feelings that are shared broadly between human nature and histories. This is not about me. This is about us. My paintings are not about Negar and what she has experienced as an individual, rather the work is about being a human carrying shared experiences. I use multidisciplinary practice to explore memory, displacement, and the layered process of healing. This body of work explores recurring themes related to fragmentations of the body, emotion, experience of trauma and healing and relates these to material expressions in painting, printmaking and assemblage that draws on ideas of destruction, through fire, tearing, decay, burning, fragility and fragmentation as traces of collapse amidst the process of construction. Together within a narrative of poetic documentation of studio process or of ‘prose-cess’ an overall feeling takes shape, characters are created and a scene is formed. And Scene…
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.208 | 0.073 |
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".