Metamorphosis & the Therapeutic Function of Storytelling: The Arabian Nights Vs. Franz Kafka’s Animal Stories
Bibliographic record
Abstract
Abstract Story telling in the book of A Thousand and One Nights , functions as a way sublimating Shahrayar’s desire for murdering Sharazad and releasing it through catharsis. i.e. the stories function as a therapy for Sharayar and as a brain-washing mechanism through which Shahrazad hopes to cure Sharayar from his murderous desire that has led to the deaths of many women because of his wife’s act of infidelity. In this article, I will highlight the similarities between some of the stories in the book of A Thousand and One Nights which focus on ingratitude and metamorphosis as dominant themes and some of Franz Kafka’s animal stories (The Metamorphosis , A Hunger Artist, and A Report to An Academy) which share the aforementioned themes. By doing so, I hope to prove that there is a good reason to assume that Kafka was, at least indirectly, influenced by the book of A Thousand and One Nights which, to the best of my knowledge, is something that haven’t been done before. Key words : Arabian Nights; Franz Kafka; Storytelling & Therapy; Postcolonial Criticism Resume Raconter des histoires dans le livre de Mille et Une Nuits, les fonctions sont comme un moyen de sublimer le desir de Shahrayar pour le meurtre de Sharazad et de la liberer par la catharsis. a savoir la fonction des histoires comme une therapie pour Sharayar et comme un mecanisme de lavage de cerveau a travers lequel Shahrazad espoirs de guerir Sharayar de son desir meurtrier qui a conduit a la mort de nombreuses femmes en raison d'un acte de sa femme d'infidelite. Dans cet article, je vais souligner les similitudes entre certaines des histoires dans le livre des Mille et Une Nuits qui se concentrent sur l'ingratitude et de la metamorphose comme themes dominants et certains des histoires d'animaux de Franz Kafka (La metamorphose, un artiste faim, et un rapport une academie) qui partagent les themes susmentionnes. En faisant cela, je l'espere, pour prouver qu'il ya une bonne raison de supposer que Kafka etait, au moins indirectement, influence par le livre des Mille et Une Nuits qui, au meilleur de ma connaissance, c'est quelque chose qui n'ont pas ete fait auparavant. Mots-cles: Mille et Une Nuits; Franz Kafka; Contes & Therapie; Critique post-coloniale
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".