Against the Grain: Portugal and Its Empire in the Face of Napoleonic Invasions
Bibliographic record
Abstract
Writing in 1846, Caetano Lopes de Moura, by birth a subject of the Portuguese Empire, expressed his deep admiration for the French emperor in his Histoire de Napoléon — a biography in which the glorious events of the past were celebrated according to a certain model of nineteenth-century historiography. 1 The most puzzling thing is that the author, who came from a modest social milieu, was in fact of metis or mixed-race origin, born in 1790 in Bahia, Portuguese America. At the time of the book’s publication he had been living in France for quite some time. Relating the Battle of Wagram he mentions that he ‘was present at this unforgettable battle as a surgeon-major in the Portuguese Legion’. In his short account of meeting Napoleon at Ebersdorf he notes enthusiastically that ‘his eyes were so full of life that anyone who looked into them was obliged to lower their own to the ground, such was the fire given off. The enthusiasm is still more manifest when describing the fall of the French emperor: ‘The great man has fallen…but not really brought down from the eminent position he held and will continue to hold in history… he has kept all his glory, all his genius, and all his moral greatness.’ Caetano Lopes de Moura never went back to Brazil and spent the rest of his life in France. He died in 1860, after a lifetime dominated by two great figures, Napoleon and Pedro II, Emperor of Brazil. 2 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".