Bibliographic record
Abstract
Harkening back to Renaissance Europe's wunderkammer, or cabinets of curiosities, Amerasia is conceived as a "preliminary cabinet display" of artifacts (15).Unlike the eclectic mishmash of objects that characterized many cabinets of curiosities, however, a common thread unites the phenomena in this book: they all attest to a European imaginary in which the Americas were intimately associated with Asia and, in some cases, Africa (15).Elizabeth Horodowich and Alexander Nagel argue that the propensity to conflate one continent with the other was not, as has often been argued, a consequence of a homogenizing European gaze.Rather, artifacts interchangeably denominated as "Chinese, " "Indian, " and "Mexican"-such as a Mexican shield dating from around 1500, the provenance of which was updated several times in subsequent centuriesevidence contemporary beliefs of a new world that was at the same time part of Asia.One by one, Horodowich and Nagel turn their attention to a variety of artifacts-above all paintings, maps, and woodcuts-that illustrate the different ways in which the Americas appear in the Far East and the Far East in the Americas.While the details of this relationship vary from one object to the next, collectively they substantiate Amerasia's central argument: for centuries after their discovery, American lands and peoples were integrated into a model of the world which asserted geographic and cultural proximity between them and their transpacific neighbours.Amerasia is an important contribution to a growing field of scholarship on how early modern Europeans attempted to make sense of the Americas within a global order that included Asia and Africa.The book joins Ricardo Padrón's The Indies of the Setting Sun: How Early Modern Spain Mapped the Far East as the Transpacific West (Chicago: University of Chicago Press, 2020) in challenging the reader to imagine the world as it was understood in the early modern period.In so doing, Horodowich and Nagel point to the pitfalls of a backwards gaze that assumes historical inevitability.It is all too easy to project our knowledge of the Americas-a separate landmass an ocean away from Asia-onto the works of early modern historians, artists, and cartographers.Yet even though the lands on early modern maps might assume similar contours to
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".