Got it Almost Right Review of Christopher Chase-Dunn
Bibliographic record
Abstract
At the annual meetings of the American Sociological Association in Toronto in 1997, Christopher Chase-Dunn was an invited critic in a session on Andre Gunder Frank’s forthcoming book Reorient: Global Economy in the Asian Age (1998), which argues the case for the dominance of Asia in the world-economy between AD 1400 and 1800. Chase-Dunn entitled his talk, ‘How Gunder Frank Got it Almost Right’. A similar title seems eminently appropriate for a commentary on Chase-Dunn and Thomas Hall’s recent book Rise and Demise: Comparing World-Systems (1997). Thus the title ‘How Chase Dunn and Hall Got it Almost Right’. This title is a high compliment on their work, for in the social sciences no one gets it completely right, and few get it even half right. Most get it wrong (often completely wrong). I read the very first draft of this book in manuscript in 1989. At that time Chase-Dunn was the sole author and he had written a very short manuscript of about 175 pages. I reviewed the book for a publisher, recommended publication, and thought the book would appear sometime in 1990, or by early 1991 at the latest. It didn’t. Chase-Dunn had bigger aspirations for the book and asked Hall,
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".