Pluralities of History: Carrying Folklore Across Languages and Cultures
Bibliographic record
Abstract
If history is a distilled collection of stories that was handpicked by colonisers, victors, and those in power, then folklore is a pool of abundant and overlapping remembered pasts from the common person. My research considers the possibility of folklore being a truer, more relevant version of history by analyzing written and oral stories from the province of Sindh. Research took place in two simultaneous phases: 1) Interviews in Karachi with people in the cultural sector, such as a literary scholar, author, musician, dancer, and archeologist to investigate the relationship between folklore and artistic and cultural practices; 2) Field visits to archaeological sites, shrines, and historic monuments that involve talking to locals about the presence of folk stories in their communities. Methods of carrying on folklore are interdisciplinary and overlapping. They include singing verses that detail stories at shrines, incorporating stories into new music, interpreting stories through dance and theatrical performance, and preserving archaeological sites. Folk stories are allowed to be multiple, overlapping, and contradicting, as the reality that they present is of a plural, collective memory. They belong in literature as much as they belong in anthropology, music, performance, and other disciplines. These pluralities are present in the interviews and field research, which will be shared and discussed, showing how the version told by an expert in the field of literature was just as valid as the story shared by a villager in interior Sindh.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.019 | 0.036 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| 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".