Biographer’s Appraisal in Joko Widodo Biography “Man of Contradiction and the Struggle to Remake Indonesia”
Bibliographic record
Abstract
This study explores the biographer’s appraisal towards Jokowi’s contradiction and struggle to remake Indonesia. The data were obtained purposively from the chapter five of the biography “Man of Contradiction: Joko Widodo and the Struggle to Remake Indonesia” (2020) and were analyzed by applying Martin and Rose’s appraisal framework (2007). The findings reveal that positive judgments were used to assess Jokowi's actions when he became his company CEO, business association chairman, Surakarta mayor, Jakarta governor, and Indonesia president; and negative judgments to criticize his leadership style and his administration’s weaknesses and strengths. The positive appreciation was given to Jokowi’s efforts on his decision to build infrastructures throughout Indonesia, while negative appreciation on Jokowi's administration that was deemed to have covered up cases of deaths and victims of the COVID-19 pandemic. In addition, to indicate his feelings towards Jokowi, the biographer uses affect that covers the desires, self-confidence, worry, fear, happiness, sadness, security, insecurity, and unhappiness Jokowi experienced during his presidential administration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".