Bibliographic record
Abstract
The cliché is still lively: historians, as is well known, tend to portray themselves as craftsmen or artisans, mastering a practical know-how learned patiently through hands-on experience with dusty documents, and showing a conspicuous disdain towards theory and abstractions. This image deserves closer scrutiny. It is interesting that despite this insistence on the craftlike image of the profession, there seems to be a lack of ethnographic investigations of historians at work that would precisely pay attention to the craftiness of history and the multiple practicalities of doing history across different contexts. The idea that historians just do what they do sounds simple enough, but as is the case with any “craft,” from basket weaving to hunting in the rainforest, it is hardly self-evident, either technically or sociologically. To be sure, there are plenty of biographies, autobiographies, “ego-histories,” methodological primers and epistemological essays that tackle and debate the problems of the working historian, but these reflexive narratives remain essentially vertical. Taking our cue from some of the recent developments in science studies and the anthropology of science, we would like to propose in this article a program for a horizontal study of historians, that would be independent of their own reflexive discourse and symmetric in its explanations, and that would be attentive to the varieties of their existence and their becoming in a community of practice.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".