Bibliographic record
Abstract
I have the pleasure of writing to Folklorica's readers from an extended research trip in the northwestern Krajina region of Bosnia and Herzegovina.On my way into the field, I stayed in Zagreb, Croatia, where I was once again provided access to the great wealth of data housed in the archive of the Department of Ethnology and Folklore at the Croatian Academy of Sciences and Arts (Hrvatska akademija znanosti i umjetnosti, hereafter HAZU).One of my current research topics involves a significant folklore collection project conducted in this region at the end of the nineteenth century.Between the autumns of 1886 and 1888, (1) the Croatian lawyer and ethnographer, Luka Marjanović-leading a small team with a shifting roster of students, writers, and scholars from Zagreb and other regions-collected 290 epic songs (and thirty lyric songs) from thirteen Muslim singers from the Bosnian Krajina.(2) Some of these songs were recorded in the Krajina on five separate fieldtrips, some were submitted by correspondents, and a large number were collected from three singers who were brought to Matica's main offices in Zagreb to record their songs and have their photographs taken.Marjanović's star singer, Mehmed Kolaković, also performed for an audience of Zagreb's high society.Over the next ten years Marjanović pored over these works, comparing variants, compiling a rich dictionary of unfamiliar terms in the songs, and selecting what he deemed to be the fifty finest to be published in the third and fourth volumes of Matica's Hrvatske narodne pjesme [Croatian Folk Songs] collection (1898 and 1899).Although the project and subsequent publications were heralded at the time for their rigor and exactitude, subsequent scholars reviewing the manuscripts were shocked to learn the drastic alterations Marjanović applied to the songs to bring them in line with his literary aesthetic [Krstić 1956; Lord 1991: 35-36, 125; 1995: 16-18, 223;Mučibabić 1981].I will have more to say on this in upcoming publications.Whatever his failings as an editor, though, Marjanović was a scrupulous collector for his time.Certain impositions he likely enacted on the singers during their performances cannot be removed from the collected manuscripts.But, otherwise, given that no audio recording technologies were yet available and all these songs were transcribed with pencil and paper, the manuscripts are a testament to the highly empirical collection regimen imposed by the scholar.For nearly every song collected, HAZU retains an original copy (pencil on quire) recorded during performance, which was then re-written in ink on a separate document.The originals were then carefully archived, largely unmolested, and all edits and alterations were applied only to the second copiesand those, intentionally, in such a way that the original verses are mostly legible.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.023 |
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".