MOTIVE AND CHARACTER EDUCATIONAL VALUES IN NOVEL THE INVENTION OF HUGO CABRET BY BRIAN SELZNICK
Bibliographic record
Abstract
<p>This is a descriptive qualitative research that uses a psychological approach. The data source of this research is the novel The Invention of Hugo Cabret by Brian Selznick, publisher of Scholastic Press, New York (original version) in 2007, Mizan Fantasi (Indonesian version) in 2012. The research data are in the form of words, phrases and sentences that related with the main character's motives and the character education values in the novel. Data obtained by reading and note taking techniques. This objective of this study to analyze the existence of the main character's action motives and the value of character education in the novel The Invention of Hugo Cabret by Brian Selznick. The results of the analysis obtained the following conclusions: 1. Hugo's motives as the main character there are 20 quotes, namely as follows: a) motives for physical needs there are 3 quotations, b) motives for security and safety needs are 4 quotations, c) motives for trust and compassion consist from 5 quotations, d) the motive for self-esteem needs there are 3 quotations, e). The motives for self-actualization needs are 6 quotations. The prominent action motive is the motive for self-actualization needs. 2. The value of character education contained in Brian's novel The Invention of Hugo Cabret, consisting of; a) religious 2 quotes, b) honest 4 quotes, c) discipline 3 quotes, d) hard work 7 quotes, e) creative 2 quotes, f) independent 1 quote, g) curiosity 4 quotes, h) appreciate achievement 2 quotes, i) friends 3 quotes, j) peace love 1 quote, k) likes to read 2 quotes, l) social care 4 quotes, m) responsibility 2 quotes.</p>
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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 | 0.006 |
| 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 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".