Bibliographic record
Abstract
Integrated paintings are paintings that distinguish themselves from non-traditional materials. It is the use of newspapers, and magazines to make collages and paper cutouts which are not normal painting works of art. The main feature of integrated painting is the variety of materials chosen and the mix and match of multiple materials used. In contemporary art, the use of materials has been reflected in a variety of paintings. In addition to natural materials, the materials created by modern technology also bring more possibilities for the creation of paintings. The different materials and their multiple uses together are a test of the artists expressive use of materials. Several artists also use unique materials so that the emotions they express in their works are expressed in the materials through materialization. This paper focuses on the diversity of mixed media and how it can bring more versatility and emotional expression to artworks. And it also analyzes the role and influence of mixed media as paintings by examining Pablo Picassos Still-Life with Chair Caning and Marcel Duchamps The Bicycle Wheel to analyze the role and impact of mixed media as a painting. Through analysis, it is also argued that the use of mixed media brings more emotional responses to the paintings and that the artists emotions can be understood and felt through the materials.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".