Bibliographic record
Abstract
American Folklore consists of traditional knowledge and cultural practices engaged by inhabitants of the United States below Canada and above Mexico. American folklorists were influenced by nineteenth-century European humanistic scholarship that identified in traditional stories, songs, and speech among lower class peasants an artistic quality and claim to cultural nationalism. The United States, however, appeared to lack a peasant class and shared racial and ethnic stock associated in European perceptions with the production of folklore. The United States was a relatively young nation, compared to the ancient legacies of European kingdoms, and geographically the country’s boundaries had moved since its inception to include an assortment of landscapes and peoples. Popularly, folklore in the United States is rhetorically used to refer to the veracity, and significance, of cultural knowledge in an uncertain, rapidly changing, individualistic society. It frequently refers to the expressions of this knowledge in story, song, speech, custom, and craft as meaningful for what it conveys and enacts about tradition in a future-oriented country. The essay provides the argument that folklore studies in the United States challenge Euro-centered humanistic legacies by emphasizing patterns associated with the American experience that are (1) democratic, (2) vernacular, and (3) incipient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.052 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".