Bibliographic record
Abstract
My research project studied the relationship between digital collage and oil painting. My process involved creating digital collages in Photoshop which were used as source images for my paintings.\nThe elements in the collages I made were tied together by all being subjects I had seen, encountered or interacted with that I felt were important enough to document. I chose images relating in visual elements and emotional qualities which I used to create my compositions.\nThroughout my research I created five paintings and ten collages, each of which taught me very different things. The most enlightening piece of the process however was the transformation of the digital collages into paintings and the interaction between the two media and art making methods. The composition as a whole remained the same however elements needed to be changed because of the limitations of painting. Colors had to be changed, visual accuracy was at times sacrificed, and different types of mark making were introduced. My biggest challenge was having to let go of pieces of paintings before I thought they were completed.\nIn my researched I learned about the give and take between the two processes and after working on the initial paintings, I changed the way made the collages as source images to be more conducive to painting. I became more familiar with the very intimate and specific relationship between the two media and how I liked to approach it.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".