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Record W7093897732

Stereotyped expressions in Sanskrit and Latin text of Bālakāṇḍa

2019· article· hr· W7093897732 on OpenAlexaboutno aff

Bibliographic record

VenueODRAZ (University of Zagreb Faculty of Humanities and SocialSciences) · 2019
Typearticle
Languagehr
FieldArts and Humanities
TopicIndian and Buddhist Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOrthographySocial power
DOInot available

Abstract

fetched live from OpenAlex

Epski bardovi, pjevači priča, sastavljali su pjesme pomoću formula i formulaičnih izraza – obrazaca riječi koji pristaju u određene dijelove u stihu i koji se po potrebi mogu kratiti, duljiti i zamijenjivati, tj. prilagođavati metru i tijeku priče. Nakon preglednog prikaza odabranih istraživanja formulaičnosti u jugoslavenskim, grčkim i indijskim epskim tekstovima, u radu propitujem teoriju Milmana Parryja i Alberta Lorda, po kojoj su formule isključivo alati pjevača za popunjavanje stihova, bez značaja za kontekst u kojemu se nalaze. Pitanje na koje tražim odgovor jest da li je to stvarno tako, odnosno na koji način su ogromni tekstovi poput Rāmāyaṇe (24.000 strofa) i Mahābhārate (oko 100.000 strofa) mogli biti prenošeni usmeno, a opet zadržati svoju književnu vrijednost i u kojoj se mjeri Parry-Lordova teorija može primijeniti na sanskrtske epove. Za analizu teksta odabrala sam prvu knjigu Rāmāyaṇe, Bālakāṇḍu, verziju koju je za tisak uredio njemački filolog August Wilhelm von Schlegel. Kroz analizu formulaičnosti 50 najčešćih riječi ukazala sam na neke osobitosti sanskrtskog jezika koje pomažu u sastavljanju epske pjesme. Potom sam kroz odabrane primjere formula tipa imenica-epitet koje se u Bālakāṇḍi koriste za jednu vrstu mitskih bića, rākṣasa, pokušala pokazati da za autora Rāmāyaṇe, Vālmīkija, epiteti nisu samo alati, nego da ih vješto koristi u okvirima postojećih konvencija. Budući da je Schlegel u prvoj polovici 19. stoljeća preveo Bālakāṇḍu na latinski, usporedila sam njegovu verziju sanskrtskog teksta s latinskim prijevodom, te analizirala dijelove o rākṣasama, i pokušala otkriti je li primijetio formulaičnost sanskrtskog teksta, je li prevodio epitete uvijek na isti način ili se trudi prevesti i nijanse u značenju, na koji način ih prevodi i zašto. U analizi sam koristila kompjuterski alat za obradu teksta AntConc, a kako bi kompjuterska analiza bila moguća izradila sam elektronički tekst Schlegelovog sanskrtskog i latinskog izdanja Bālakāṇḍe.

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)0.004
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: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.006
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.043
GPT teacher head0.221
Teacher spread0.178 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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