"The Grass is Always Greener" and "Questions for AI"
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As a third-year undergraduate student studying Interactive Systems Design, Kate’s art explores a variety of mediums, in which a focus on the creative aspect of perspective is carried throughout her work. With Kate’s complementary interests in art and technology, she primarily experiments with graphic design, digital illustration, photography, and a combination of these mediums in her projects. "The Grass Is Always Greener": This piece is a digital print created using Adobe Photoshop that parodies the well-known “grass is greener” proverb. "Questions for AI": There are some questions out there that are hard to think about or ask. Perhaps even some that we will never have definitive answers for. However, as technology continues on its course of rapid advancement within the landscape of our ever-evolving society, these are the questions that stand between us and our understanding of where the future of humanity lies. As such, “Questions for AI” is a digital print series that prompts viewers to reflect not only upon recent modern development in artificial intelligence, but also on the new realities of the human race.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it