History from within? Contextualizing the new neurohistory and seeking its methods.
Bibliographic record
Abstract
"Histories from below" sought to give voice to those ordinary folk whose social position had failed to afford them great power, wealth, or responsibility: the neglected undocumented. Now, Lynn Hunt (2009) calls for a revolution that would task historians with giving voice to feelings--what I will call a "history from within." This is what led her to endorse Daniel Lord Smail's (2008) suggestion that historians appeal to neuroscience and thereby construct a "new neurohistory." The purpose would be to introduce a common factor to all human stories: a tool to think with when describing what it was like (cf. Nagel, 1974). If successful, this would be quite powerful: in Hunt's view, such a project could lead to a universalization of human rights. But the program is not without challenges, one of which is to provide an acceptable explanation for the type of looping causation that applies to bio-cultural kinds. Smail's solution involves an appeal to evolutionary theory, but how this solves the problem of causation is not clear. Here, therefore, an attempt is made to clarify his solution. Smail and Hunt's views on the role of evidence in history are also made plain. The paper then concludes by importing related ideas from the recent history of philosophy. If one is going to have a brain-based view of felt-history, then the neurohistorian's task is to situate historical individuals in contexts of shared experience--to not just read evidence through lenses of intellectual "thought collectives" (generalized from paradeigma), but also through "experiential" or "moral categories" (aisthánomai).
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.083 |
| Scholarly communication | 0.013 | 0.033 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| 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".