Distributed Cognition in Enlightenment and Romantic Culture
Bibliographic record
Abstract
11 essays by international specialists open up the research field of distributed cognition and the cognitive humanities in the Enlightenment and Romantic periods The third book in an ambitious four-volume set looking at distributed cognition in the history of thought Brings together essays on literature, history, philosophy, art, archaeology, medicine, science and material culture Includes a general and a period-specific introduction to distributed cognition and the cognitive humanities For students and scholars in Enlightenment and Romantic studies, cognitive humanities and philosophy of mind Draws out what was distinctive about Enlightenment and Romantic insights into the cognitive roles of the body and environment Examines how humanities topics are affected by new insights from the cognitive sciences This collection explores how Enlightenment and Romantic practices and ideas reveal the diverse ways that cognition was seen as spread over brain, body and world in the long 18th century. Contributors Miranda Anderson , University of Edinburgh and University of Stirling, UK. Ros Ballaster , Mansfield College, University of Oxford, UK. Renee Harris , Lewis-Clark State College in Lewiston, Idaho, USA. Elspeth Jajdelska , University of Strathclyde, UK. Karin Kukkonen , University of Oslo, Norway. Charlotte Lee , University of Cambridge, UK. Jennifer Mensch , Western Sydney University, Australia. Lisa Ann Robertson , University of South Dakota, USA. George Rousseau , University of Oxford, UK. John Savarese , University of Waterloo, Ontario, Canada. Richard C. Sha , American University, Washington DC, USA. Helen Slaney , University of Roehampton, UK. Mark Sprevak , University of Edinburgh, UK. Michael Wheeler , University of Stirling, UK.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".