Translation and Historical Stereotypes: The Case of Pedro Cieza de León’s Crónica del Perú
Bibliographic record
Abstract
Translation and Historical Stereotypes : The Case of Cieza de Leon's Crónica del Perú — The Crónica del Perú (books I and II) by Pedro Cieza de León (1553) is one of the most systematic and objective descriptions of the Spanish conquest of America. It is also one of the best written. The book was first translated into English by Captain John Stevens in 1709, then by Sir Clements R. Markham in 1864 for the Hayklut Society, and finally by Harriet de Onís in 1959. However, none of these translations does justice to Cieza's magnificient work. While the two first translations are full of mistakes, acknowledged and unacknowledged omissions, as pointed out by Diffie, 1936; Bernstein and Diffie, 1937 and Pedro R. León, 1971, the third attempts a conflation of the two books into one, resulting in a confusing edition not devoid of misprints and inaccuracies. This paper attempts to show how the English translations of the Crónica, by way of unfortunate or deliberate manipulations aiming to obliterate the objectivity of Cieza's writing, have contributed to the reinforcement of the stereotypes which shape the "Black Legend" of the Spanish conquest of the New World. Stereotypes that, in the light of examples like this, perhaps need to be redefined.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".