Bibliographic record
Abstract
China has changed a great deal over the last few decades, of course. That change continues, and the pace of that change continues to acceler ate. While I was in Nanjing, the government announced China's seventh consecutive quarter of double-digit GDP growth; soon after, PetroChina's IPO produced the world's largest company in market value, double that of the next-largest, Exxon-Mobil. Whether such growth can continue remains to be seen. Whether they can avoid, or even mitigate, their loom ing environmental disaster also remains to be seen. Facing a potentially perilous future, history becomes that much more vital, but the questions I addressed in the course raised a number of issues of relevance not just to historians, but also to those who teach humanities, particularly in a context so distinct from a more customary situation in the West. The course I designed was intended to explore the philosophical background of what drove the North American colonists to declare their independence; what ideas informed the writing of the Declaration of Independence, the Constitution, and the Bill of Rights; and what models competed in determining the state envisaged. Although my professional
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".