NILAI MORAL DALAM BUKU CERITA ANAK HIKAYATU JUHAWA AL-HIMAR WA HIKAYATU UKHRA KARYA MANSHUR ALI IRABY (Kajian Resepsi Sastra Wolfgang Iser)
Bibliographic record
Abstract
Tesis ini mengkaji tentang sastra anak, khususnya sastra anak Arab (Arabic Children’s Literature) dalam ranah kajian aplikatif. Tesis ini membahas tentang nilai moral atau nilai karakter yang terdapat dalam buku cerita anak Hikayatu Juha wa al-Himar wa Hikayatu Ukhra karya Manshur Ali Iraby dengan menggunakan teori resepsi sastra Wolfgang Iser, dimana penulis akan memposisikan diri sebagai real reader (pembaca) agar dapat mengeksplorasi nilai-nilai moral yang ada dengan leluasa dan lebih mendalam. Penelitian ini merupakan jenis penelitian kepustakaan atau library research dengan menggunakan metode analisis isi. Hasil penelitian menunjukkan bahwa nilai moral yang ditemukan dalam buku cerita anak Hikayatu Juha wa al-Himar wa Hikayatu Ukhra berdasarkan resepsi penulis sebagai pembaca (real reader) terbagi menjadi empat bagian sesuai dengan kategori hubungannya, yaitu sebagai berikut: 1) Hubungan manusia dengan Tuhan: berupa nilai moral religius; 2) Hubungan manusia dengan diri sendiri: percaya diri, sabar, tanggung jawab, bersyukur, jujur, kreatif (pikiran dan tindakan), teguh pendirian, rasa ingin tahu, dan teliti; 3) Hubungan manusia dengan sesama: toleransi, demokratis, berbakti kepada orang tua, dan juga peduli sosial; 4) Hubungan manusia dengan lingkungan: memperlakukan makhluk hidup dengan baik, dan menyayangi hewan dengan tulus.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.099 | 0.027 |
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".