Making the World: Pictures and Science in Modern China
Bibliographic record
Abstract
How do pictures make the world? In 1907 the Chinese fiction writer and social critic Lu Xun 魯迅 (1881-1936) essayed the thought that making the world depended on attunement towards beauty’s emotional vibrancy and on an imaginative frequency to scientific thought, both. This curatorial project asks after pictures made mostly during Lu Xun’s lifetime, in turn-of-the-century China, mostly by brush-and-ink painters, but also by embroiderers, photographers, cartoonists, taxidermists, map-makers, and others who worked self-consciously within the arts and sciences, popular or academic. Their pictures carry within them their own struggles with the rationalities of science, as well as emotions and imagination, to make the world. Still, as curators of each of the six thematic sections of the exhibition observe, the pictures also escape the hands of their makers; they are thrown back into the flow of time through the questions they pose of us now, questions that we hope will prompt us to see and sense nature and each other differently, and in doing so, to make our own world from a newly aware and nuanced perspective.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".