Brazil’s Historians in North America, 1980-2019: A Survey of Their Careers
Bibliographic record
Abstract
This article examines the professional careers of the 290 historians who received doctorates in Brazilian history from universities in Canada and the United States between 1980 and 2019. It is a follow-up to a 1990 study by Roderick J. Barman on North American historians of Brazil from 1950 to 1987. While the 1980s were a nadir for the field, historians of Brazil enjoyed unexpectedly good academic career outcomes in the 1990s and early 2000s; they continued to do well in the academic job market, while many of their dissertations were published. The data also reveal some enduring patterns when it comes to the chronological periods and geographical areas on which these historians focus, as well as the rising interest in post-1945 history. The proportion of women winning doctorates has stabilized at levels slightly higher than that of the profession as a whole; however, some small but troubling gender inequities persist.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".