The Setting in the Novel of Al-Tanturiya
Bibliographic record
Abstract
This study deals with the setting and its connotations in Al-Tanturiya novel. The setting dominates the novel from beginning to end. It also highlights the effect of the setting on characters, their behaviors, morals, customs, traditions, and reactions. Moreover, this article proves that place remains unforgettable and stored in man’s memory throughout his lifetime. It is always the incidents and events that that rummage in the memory and reveal the memories of settings with its signs, significance, and symbols. The setting in this novel comes in a variety of forms such as the countryside, the city, and the immigration country: (Lebanon, UAE, Egypt, and Canada). This diversity imparts to the text an aesthetic embodiment that involves the patterns of the setting and its implications in the novel. This research is based upon the methodological approach by extracting the spatial artistic structures, analyzing them, showing their aesthetics, meanings, and impacts on the text. The study will cover different elements in the novel such as: 1. An introduction that presents a short overview about the novel and its emergence; 2. The setting with its significations and patterns through different and diverse places in the novel such as the countryside, the immigration countries of Lebanon, Egypt, UAE, Canada; 3. The impact of the setting and its reflection on man and his surroundings; 4. The mechanisms of artistic embodiment of the setting and its aesthetics by shedding light on the aesthetic element of language and its manifestations in the text. Key Words: Place, Al-Tanturiya, Stylistic Approach
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".