Morlaci kao ekonomsko drugo u djelima Alberta Fortisa i Ivana Lovrića
Bibliographic record
Abstract
Sažetak Cilj rada jest ponuditi nove perspektive u analizi morlakizma kao diskursa unutar metodoloških i teorijskih odrednica historijske imagoogije, na primjeru dva poznata prosvjetiteljska izvora: Putu po Dalmaciji (1774.)Alberta Fortisa te Bilješkama o Putu po Dalmaciji i Životu Stanislava Sočivice (1776.)Ivana Lovrića.Iako je historijska imagologija otvorila nove perspektive pri izučavanju Vlaha/Morlaka unutar historiografije, velik dio imagoloških radova nije uključivao ekonomski pogled na morlakizam, već se ponajviše fokusirao na koncepte poput civilizacije, barbarstva i »plemenitog divljaštva«, bez kritičkog propitivanja tih koncepata.Glavni je fokus ovog rada stoga analiza morlakizma kao diskursa uz poseban naglasak na fiziokratizam kao ekonomsku doktrinu čiji se utjecaj može vidjeti u tekstovima Fortisa i Lovrića.Morlaci su kao ekonomsko Drugo na razini teksta analizirani kroz topose ekonomske iracionalnosti i zaostalosti te kroz dodatnu analizu slike priobalnog stanovništva Dalmacije.Ova se kompleksna slika o Morlacima kao ekonomskog Drugog zatim pozicionira unutar intertekstualnih odnosa: između tekstova Fortisa i Lovrića, poznatog teksta Tableau Economicque Françoisa Quesnaya te tekstova poznatih dalmatinskih fiziokrata.Posljednja razina analize pokazuje način na koji se morlakizam uklapa u političko-ekonomski kontekst mletačke Dalmacije, uz fokus na strukturne promjene i pojedine ekonomske mjere koje je provodio mletački i dalmatinski patricijat.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
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".