Bibliographic record
Abstract
For the past five months, I have been working on researching and digitizing a set of twenty-four Chinese papercut posters at W.D. Jordan Rare Books and Special Collections. Using the web publishing platform Omeka, the project combined the digital images of the papercut posters and all the metadata including title, translation, historical background and dimensions. This set of papercuts reflects the history of the Chinese revolution from the founding of the Chinese Communist Party to the establishment of People's Republic of China. This set includes the most representative events in all stages of the revolution creating a microcosm of the history of the Chinese people seeking liberation. Among these historical events, the majority of them were also displayed in the film “The East Is Red” which is a “song and dance epic” filmed in 1965 for celebrating the 15th anniversary of the founding of the People's Republic of China. The Chinese papercut posters online collection preserves and increases accessibility to these rare materials of which there is only one other collection online. By accessing to this site, more scholars can study this unique collection without time and location limitation. Website Link: http://postercollection.omeka.net/collections/show/1
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.431 | 0.170 |
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".