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Record W1969677433 · doi:10.1353/ort.2011.0039

Matija Murko, Wilhelm Radloff, and Oral Epic Studies

2011· article· en· W1969677433 on OpenAlexaboutno aff
Aaron Phillip Tate

Bibliographic record

VenueOral tradition · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPARRYEPICScholarshipFolkloreQuarter (Canadian coin)Oral traditionHistoryLiteratureClassicsOral historyOral literatureArtLawPolitical science

Abstract

fetched live from OpenAlex

In modern histories of folklore scholarship, when the topic concerns pioneers of oral epic fieldwork prior to Milman Parry and Albert Lord, no scholars are mentioned more often than Wilhelm Radloff and Matija Murko.1 Though the two worked in different language families and belonged to different scholarly generations (Radloff was nearly a quarter-century older than Murko), the reasons for their influence are well known: Radloff was one of the first to collect oral epics from Turkic-speaking peoples in Russia and Siberia, doing so throughout the 1860s and 1870s, while Murko, in his time as a professor in Vienna, Graz, Leipzig, and Prague, conducted extensive fieldwork in Yugoslav lands among epic and lyric singers as early as 1909 and as late as 1932.2 Today both are regarded as two of the earliest observers of oral epic to have provided substantial firsthand documentary accounts of performances they witnessed in the traditions within which they worked, and both are frequently cited in debates surrounding the role played by oral epic in the twentieth-century form of the "Homeric Question."

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.017
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.446
GPT teacher head0.270
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2011
Admission routes1
Has abstractyes

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