Narrativization of history in 18th century Franciscan chronicles and in the morlachian trilogy of Ivan Aralica
Bibliographic record
Abstract
S posebnim fokusom na naratološke strategije same povijesti koja se konstruira tek pripovijedanjem (H. White), te na fikcionalizaciju historije i historizaciju fikcije (P. Ricoeur), osobita se pozornost u ovom radu posvećuje interferencijama Araličinih novopovijesnih romana s franjevačkim kronikama na kompozicijskoj i fabularnoj razini, potom poveznicama u narativnoj i metanarativnoj strukturi, u stilskom oblikovanju, u načinima inkorporiranja elemenata iz folklorne tradicije (legendi i predaja), u opisima odnosa katolika i muslimana, u predodžbama osmanlijske i mletačke vlasti i sl. Umijeće pripovijedanja o povijesnim događajima prati se dakle u trima franjevačkim ljetopisima (Benićevu, Lašvaninovu i Bogdanovićevu) i trima Araličinim romanima: Put bez sna (1982), Duše robova (1984) i Graditelj svratišta (1986). Tom se korpusu u analizi književnoga modificiranja povijesti u Araličinoj morlačkoj trilogiji pridodaje putopis Alberta Fortisa Put po Dalmaciji (Viaggio in Dalmazia, 1774) u kojemu talijanski putopisac donosi opise svakodnevnog života i običaja Morlaka, tj. kontinentalnog stanovništva mletačke Dalmacije. Teorijska okosnica diplomskoga rada poglavito je vezana uz pojmove poput fikcionalizacije historije i historizacije fikcije, kulture sjećanja i figura sjećanja te intertekstualnosti i citatnosti.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".