Bibliographic record
Abstract
Emily Copeland is a Canadian artist, specializing in realism. Mainly working with charcoal, she focuses on recreating objects on a larger scale.\nRealism, and the act of mimesis are two artistic forms Copeland finds fascinating. She uses digital photography and Photoshop to first generate her images, as photos are always her preliminary approach and stimulus when it comes to creating something original.\nShe often chooses to focus on detailed objects as her subject matter. After photographing, she draws these objects on a large scale. To iterate, her goal is to mimic the exact version of the photograph with her chosen medium, which is often charcoal.\nMany artists from the Baroque era inspire her, such as Caravaggio, La Tour, and Velazque. It is their focus on mimesis; replicating what they see; and their contrasts with lighting that draws her to their works. Her current influences are Jonathan Delafield Cook, CJ Hendry and DiegoKoi, primarily because they work from photographs to create hyper realistic works.\nIn 2014/2015 she worked with piles, or stacks, of items. These objects include poker chips, books, wood, clothing, and teacups. These elements are blown up much larger than life size to give it a surreal effect. This gives the audience a unique viewpoint that exposes detail they wouldn’t normally see. Each stack is compiled of something different; different materials, different textures, and different colours causing a variety of different shades and tones. Even though these objects are completely random, she attempted to create a pattern of shapes that change from circular, to rectangular, to triangular, then back to rectangles and circles. She often chooses to let the meaning behind each piece remain anonymous, as this gives each audience members the ability to find their own unique meaning throughout each piece. Her intentions behind this is because she thinks everyone can find their own personal interpretation to each work of art, and it should never be restricted to only one viewpoint.\nIn 2015 she has also started a vintage sports equipment theme. With each different series, she hopes to please a wide range of audiences with the objects she’s chosen.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.821 | 0.713 |
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".