Bibliographic record
Abstract
Offering a glimpse into the Chinese-diasporic experience, I create mundane artworks that elicit the sensation of reliving small joys from my life. In 饺子, or dumpling, in English, a still life drawing of a home-made Chinese pork dumpling is captioned with the word “dumpies!”. The image references a time when my siblings and I were trying to remember what the food is called in Mandarin (none of us are remotely good at the language) and, when that failed, we settled on calling them dumpies instead. The vast negative space of the drawing resonates with the simple design of the subject, gesturing to the sort of echo chamber that can happen when one is comfortable and used to being around folx with similar life experiences and worldviews. The memory of making dumplings in the kitchen with family is rather unremarkable as it was common in my community growing up. But as I embarked on my academic journey, I learned that the experience was alien to many of my peers and mentors – that sitting around a table preparing food with family wasn’t a universal practice. It was a small but disorienting realization. From this, it can be observed that language and food play integral roles as methods of retaining and preserving everyday culture among displaced communities.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".