The Global and Beyond: Adventures in the Local Historiographies of Science
Bibliographic record
Abstract
As we strive for a more polyvocal history of science, historians have placed increasing emphasis on local case studies as a way to globalize the field. This tension between the local and the global extends to the practice as well as the content of the history of science, as the field has begun to pay more attention not just to local case studies, but also to local cultures of historiography. Many historians of science want multiple historiographical voices that take seriously the concerns and literatures in different linguistic and national contexts. As more journals urge translation into what are perceived as the dominant languages in "global" humanities discourses and scholars are encouraged to facilitate the Englishing of their historical research, many questions remain: In what ways does it make sense to nationalize or culturally locate individual cultures of the history of science? Is translation a democratic and positive force in the discipline, and/or does it kill local difference? Will it continue to be meaningful to talk about the history of science? A collective dialogue within the field depends on finding a way to take local diversity in historiography and epistemology seriously while translating that local difference into a meaningful common conversation. This essay considers the challenges of embracing multiple ways of knowing that might fall under the purview of the history of science, while wondering what that means for the coherence of the field as we move forward into the next century of work in Isis.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.013 | 0.056 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".