Anne Shirley’s Characterization and Character Development as The Reflection of Her Imagination in Montgomery’s Anne of Green Gables
Bibliographic record
Abstract
This thesis is presenting an analysis of major character in the novel written by Canadian author; Lucy Maud Montgomery, titled Anne of Green Gables. Anne Shirley – the major character – is portrayed as an imaginative girl in this novel. She was living in orphan asylum before being mistakenly adopted by unmarried siblings, Marilla and Matthew Cuthbert who lived in Green Gables. This thesis will analyze Anne Shirley’s characterization and also her character development as the reflection of her imagination. Therefore the aim of this thesis is to find about Anne Shirley’s characters and character development as the reflection of her imagination. New Criticism is the theory that will be use in this thesis and only focuses on the novel instead of the author’s intention. The data analysis is descriptive-qualitative and the method in data collecting is close reading from library researches whether it is from book or online. In finding Anne’s characterization, the foundation of characterization by Richard Cohen will be use and also the stages of character development in Child psychology by Lester D. Crow are use in finding Anne’s character developments. Along in finding Anne Shirley’s character, imagination becomes her main character. The writer also finds about Anne character development, that she gradually becomes very mature and can make her own decision when finally she chooses to stay in Avonlea rather than pursuing her scholarship. But she never gives up hope and keeps on studying even if she has to do it without study under one academy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".