Orphans in Society: A Comparative Study of Gender Differences in Selected Works of Childrén's Literature (1876-1911)
Bibliographic record
Abstract
The thesis examines different representations of literary orphans in a selection of books for children published between 1879 and 1911. The four chosen texts are Mark Twain’s The Adventures of Tom Sawyer (1876), J. M. Barrie’s Peter and Wendy (1911), L. Frank Baum’s The Wonderful Wizard of Oz (1900), and Frances Hodgson Burnett’s The Secret Garden (1910). The study looks at significant gender differences in the process of the orphan’s adaptation to his/her social environment. It also shows how orphans turn their miseries into actions and how they serve their society when they are given the opportunity. The selected texts are going to be analysed according to a gender-based comparison and close reading of the journeys taken by their male and female heroes in order to prove themselves in society. There is a deep analysis of the character of the orphan taking into account three criteria: The orphan’s adaptation to the foster family and relationship with other characters (not members of the foster family), the orphan’s search for identity, and the orphan’s adaptation to the physical and cultural surrounding. These criteria are discussed according to three different approaches: ethics of care, social identity and the role of the setting.\n\n\t\t\t\t The results of this study will show whether there are significant gender differences in the way the orphan characters of the selected novels behave regarding their family, friends, their domestic environments, and in the way they forge their identity. Given the socio-historical context of the chosen novels, it is to be expected that orphan heroes should tend to be better care receivers than care givers, and rely on female characters to provide that care. It is also to be expected that they tend to be less emotional and better at exerting leadership than their female counterparts, while, concerning their relationship with “home”, one can expect that they are not ready to adapt unless they find the care and attention they need. In contrast to male orphans, we expect to find orphan heroines that excel at being care givers, even when they are in dire need of care themselves. At the same time, taking into account contemporary discourses concerning female education and position in the family, it should not be surprising if home was almost automatically connected with motherhood and domesticity.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".