PEMBENTUKAN IDENTITAS HIBRID TOKOH IMIGRAN DALAM ROMAN LANDNAHME KARYA CHRISTOPH HEIN
Bibliographic record
Abstract
Artikel ini berjudul “Pembentukan Identitas Hibrid Tokoh Imigran dalam Roman Landnahme Karya Christoph Hein”. Artikel ini bertujuan untuk mengemukakan pembentukan identitas hibrid tokoh imigran dalam Roman Landnahme Karya Christoph Hein. Metode yang digunakan dalam penelitian ini adalah metode kualitatif dan deskriptif. Penenlitian ini menggunakan teori hibriditas Bhabha (1994) dan integrasi imigran Heckmann (2015). Hasil dari penelitian ini adalah (1) tokoh mengalami pe-liyan-an karena ia seorang imigran, (2) adaptasi tokoh dengan budaya Jerman yakni melalui pengaitan diri dengan masa lalu dan peniruan budaya lain, dan (3) identitas hibrid yang dimanifestasikan tokoh yakni menggunakan dialek campuran dalam berkomunikasi. Penelitian ini membuktikan bahwa identitas adalah konsep yang cair.
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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 | 0.004 | 0.006 |
| 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 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".