Bibliographic record
Abstract
In this chapter, the focus is on oral poets and their diverse roles within different cultures and societies. The chapter begins by addressing the broad question of who the poets are, explaining that virtually anyone can assume this role depending on the social context. While oral poets can be found in various forms—from official court poets in medieval kingdoms to unpaid singers among labourers—there are recurring patterns that shape their societal roles. These poets are often bound by social conventions and are influenced by the economic and political institutions of their societies, indicating that the position of oral poets is not entirely random. This chapter further explores these positions through case studies of five distinct poets from various cultures: Velema Daubitu, a seer and poet in Fiji; Avdo Mededović, a Yugoslav epic minstrel; Johnnie B. Smith, a black American prisoner and song leader; Orpingalik, an Inuit poet and shaman; and Almeda ‘Granny’ Riddle, an American folksinger. These studies illustrate the variety of oral poets, from those who consider themselves mere conduits for ancestral voices to those who use personal experiences and social circumstances to compose their works. While each poet navigates different societal expectations and roles, their artistry is shaped by both their individual creativity and the cultural frameworks in which they operate. This reveals the complex interplay between personal expression and the collective, traditional structures that support oral poetry.
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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 | 0.002 | 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 | 0.011 | 0.019 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".