The Strains of Strife: Understanding Conflict through Irish Songs (Europe) and Assamese Folk Songs (Asia)
Bibliographic record
Abstract
Folk songs encapsulate contemporary society’s cultural life. Folk songs have been used by many historians to better comprehend the culture and traditional consciousness of people who left just a few written records of their lives. As a result, folk songs can reveal a lot about their history, culture, values, and societal advancement. Folklore is a body of expressive culture that encompasses folktales, folk music, superstitions, beliefs, and other cultural expressions exclusive to a community. A folk song, on the other hand, is a song that belongs to a community’s or region’s folk music, and can have a variety of regional features. Folklore has been classified in a variety of ways, with Dorson (1972) dividing it into four categories: i) oral tradition, (ii) material culture, (iii) social folk customs, and (iv) traditional folk arts. This paper tries to explore the changing sensibilities in popular culture, particularly in the field of folk music, and the forms in which it is expressed and used as a tool to resist the status quo. This manuscript focuses on the folk traditions of traditional Irish music and Ulster Orange music in Northern Ireland and the co-existing folk music of the Koch Rajbonshis in the Assam region of North-Eastern India, trying to highlight the attached identity of the groups/community in both regions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".