Book review: laut bercerita, the sinking of untold tales
Bibliographic record
Abstract
Leila S. Chudori or full Leila Salikha Chudori (born December 12, 1962) is an Indonesian writer who has written works such as short stories, novels, and television drama scenarios. A few notes about Leila who was selected to represent Indonesia to receive a scholarship to study at "Lester B. Pearson College of the Pacific (United World Colleges)" in Victoria, Canada. Then, he continued to work as a journalist for the news magazine Tempo. She is also the winner of S.E.A. Write Award, is a tribute to writers and poets in Southeast Asia for her novel Laut Bercerita. It felt inspired by the kidnapping and enforced disappearance cases that occurred in 1998. In fact, before writing this novel, the writer conducted research interviews with one of the activists who had been kidnapped in 1998. Laut is the main character. Tells the story of a student activist during the New Order era, who was tortured and slaughtered to give testimony, then drowned along with a story that he had not had the chance to convey to Indonesia. Not only the advantages, this book also has disadvantages such as a slow storyline that tends to make the reader feel bored. Despite its flaws, this book has epic details, the characters in each character are built strongly, the events that really unfold, the deep meaning in each poem, and the selection of the right diction.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.082 | 0.073 |
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".