Dialogue Patterns in Mahmoud Darwish’s “Mural”
Bibliographic record
Abstract
This study aims at reading Mahmoud Darwish’s collection of poems “Mural” through an artistic technique, which is the dialogue with all its different patterns and types. The study will clarify the development of Darwish’s poem and his dramatic and epic lyrical spirit, and introduce a definition of these patterns and the way the Darwishian poem extends through them, as well as how its artistic and thematic discourse matures through them, reaching the level of universal poetic experiments in form and content. After that, the deals with defining dialogue as the central element in the dramatic structure and in the research, by defining it and connecting it with the other dramatic elements such as action, event, conflict and the interactive relationship among them theoretically and practically. The study introduces the three dialogue patterns, which are: the external dialogue, the internal dialogue, and the plurality of voices. The study has deduced, through studying and analyzing the poem, the role of external dialogue in demonstrating the poet’s quotes and his poetic speech through “the other,” with its different images, in the text. By using internal dialogue the poet reveals his inner feelings and his internal conflict, and this deepens the single dialogue, rescues it from simplification and leads to plurality of track, as well as, artistic and aesthetic complexity. Using plurality of voices, the poet introduces death as a third party, with which he engages in dialogue, as death seems to be the central character in the poem which enriched the text with an attempt to confirm the ego and the self in death’s conflict with them.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".