Bibliographic record
Abstract
My first academic job in 1962 involved teaching a course on History of Civilization. We had a text that essentially involved Western Civilization with chapters on India, China, and Japan interspersed. Two years later, when I returned from my doctoral thesis research in Africa, my thesis supervisor, William Halperin, recommended me for a ten-week adult education group discussing William McNeill’s Rise of the West. I was stunned that in a history of Eurasia, McNeill devoted only five pages to Africa. The incorporation of Africa in world history has been slow. For many of us in that first generation to study African history in Europe and North America, the marginality of Africa in the study of history was sometimes what drew us to study it. (There were a small number of African-American historians who wrote about Africa, but they had little impact on history curricula outside the small world in which they operated.) As a graduate student, I did a field on the Expansion of Europe and was struck by the inferior quality of much that had been written about Africa, largely by missionaries and colonial administrators. Until the Foreign Area Fellowship Program sent me to the University of Wisconsin-Madison to “tool up” with Jan Vansina, I was oblivious to the work that scholars like Vansina, Oliver, and Curtin were doing. Once I began researching Africa, the excitement was that of creating a new field of historical research.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".