Current debates and emerging trends in the history of science in premodern Islamicate societies
Bibliographic record
Abstract
This roundtable brings together contributions from nine senior, mid-career and junior scholars who work on the history of science in pre-1800 Islamicate societies. The contributions reflect upon some of the challenges that have historically constrained the subfield, how they have sought to overcome them, and what they see as some of the more productive and fruitful turns the field has taken and/or should take in the future. A central trend in all contributions is how they seek to confront the combined weight of colonialism, Orientalism, and the teleological history of science that continues to haunt contemporary discussions in both academia and the general public with regards to science in pre-1800 Islamicate societies. Without diminishing the pioneering achievements of the generations of historians who have preceded us, and upon whose work we continue to rely, this combined weight has tended a) to marginalize the study of occult sciences in Islamicate societies; b) to emphasize investigations of content from an etic perspective of how we got to the present, which is primarily seen as how the scientific content is connected to the rise of modern science in Europe; and c) to concomitantly marginalize the study of science in post-1200 Islamicate societies, particularly those with little to no connection to the rise of "Western" science. The contributions build upon conversations that took place among participants in December 2019 at a workshop at New York University (NYU), Abu Dhabi Institute in New York City, funded by a grant from NYU Abu Dhabi.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".