The Imprint of Affect: Humor, Character and National Identity in American Studies
Bibliographic record
Abstract
What is the relationship between American studies and affective production? In what specific ways does our scholarship participate in the creation, circulation, and appreciation of affective practices? These questions provide a foundation for understanding the sometimes obscure connections between academic scholarship and mass culture. I argue that the history of American studies involves a specific and influential imbrication with affective production that has shaped notions of identity and affect since the nineteenth century. Usually this history is understood in terms of how the field used to advocate conservative notions of nativist national identity; this paper brings the history of this advocacy into new focus by histricizing the relationship between scholarship and affective production in the often-overlooked field of humor studies. The first section traces the invention of an academic tradition that articulated humor practice to national character, and identifies this articulation itself as the affective labor of that scholarship. The second section addresses alternative histories that might be written once we recognize this articulation of affective practice to identity as itself a form of affective labor. In three case studies, I briefly explore the relations between humor, mass culture, and politics in the works of the late nineteenth-century humorists David Ker, Marietta Holley, and Bill Nye, whose humor was produced in the same period that saw the durable articulation of humor practice to national identity emerge. These cases gesture, polemically, to the important work American studies can still do with humor, especially as we realize the key role of affective production in our disciplinary history.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".