Bibliographic record
Abstract
Teatrologi se v svojih razpravah o kanonizaciji komedije navadno zanašajo na analize uspešnih besedil. Toda prav tako produktivno, če ne celo zanimiveje, bi bilo, če bi odgovore na svoja vprašanja iskali pri neuspešnih komedijah, še zlasti pri tistih, ki so jih napisali vrhunski pisatelji. V pričujočem prispevku se zato osredotočam na tri igre iz približno istega zgodovinskega obdobja, ki so ob praizvedbi klavrno propadle in si od poloma bodisi nikoli niso popolnoma opomogle ali pa jih od takrat vsaj ne štejemo več za komedije. Ta tri dramska besedila so: Kandidat (Le Candidat [1874]) Gustava Flauberta, Guy Domville (1894) Henryja Jamesa in Utva (Ча́йка [1895]) Antona Pavloviča Čehova. Čeprav so bila ta besedila uprizorjena v času, ko so bili njihovi avtorji že dodobra uveljavljeni, se premiersko občinstvo nanje vendarle ni odzvalo v skladu s pričakovanji. To je še presenetljivejše zato, ker sta tako Flaubert kot Čehov sicer napisala kar nekaj zelo zabavnih besedil. Da bi ugotovil, zakaj se je to zgodilo, sem izbrane igre primerjal z nekaj drugimi: s Sternheimovo priredbo Flaubertove komedije, z Wildovo Nepomembno žensko in z dvema zgodnjima burkama Čehova. Rezultati primerjav nakazujejo, da je najverjetnejši vzrok za polom vseh treh komedij neuravnovešen odnos med razumom in nerazumom, ki jih načeloma dela preveč negativne, da bi v njih lahko zares uživali.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.109 | 0.039 |
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".