Literary Discourse: An Investigation into Semiotic Perspectives of Persian Narratology
Bibliographic record
Abstract
This study was to approach critical discourse analysis (CDA) from a semiotic perspective. A case in point was Persian anecdotes. The data included four anecdotes randomly extracted from the book Stories of Bohlool. Data analysis was done within the framework of narrative semiotics of Greimas (1986). The anecdotes were analyzed in terms of 1) applicability of Greimassian approach to Persian anecdotes and 2) different levels of meaning latent in literary texts. The results of the study indicated that Greimassian approach is applicable to Persian narratology. Key words : CDA; Semiotic Perspective; Persian Anecdotes; Narrative Semiotics of Greimas Resume: Cette etude tente d'aborder l'analyse critique du discours (ACD) dans une perspective semiotique. Un exemple en a ete anecdotes persiques. Les donnees comprenaient quatre anecdotes tirees au sort dans le livre Histoires de Bohlool. L'analyse des donnees a ete realisee dans le cadre de la semiotique narrative de Greimas (1986). Les anecdotes ont ete analysees en termes de 1) l'applicabilite de l'approche greimassienne d'anecdotes persiques et 2) les differents niveaux de sens latent dans les textes litteraires. Les resultats de l'etude ont indique que l'approche greimassienne est applicable a la narratologie persique. Mots-cles: ACD; Perspective SEmiotique; Anecdotes Persiques; SEmiotique Narrative De Greimas
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".