An Interpretation of the Characters in Lessing’s Fictions From a Feminist Perspective
Bibliographic record
Abstract
Doris Lessing’s attitude towards feminism and her writing about it always has a sense of ambiguity. Lessing’s characters have not only obvious feminist features but also a very feminist title: Free women. One can find clear feminist features in Lessing’s female protagonists from either The Golden Notebook or The Summer Before the Dark. They are all independent, anti-man and longing for freedom. They either get divorced with children (like Molly Jacobs and Anna Wulf) or stay as free as unmarried within marriage (like Kate Brown). Their independence, hostility against men and desire for freedom are the seminal features of feminism. And the self-discovery of these female characters is at the same time Lessing’s own exploration into feminism itself. Besides, the male characters in Lessing’s fictions have an equal importance in suggesting Lessing’s feminist ideas. In Anna’s words, women, especially woman writers, create their men in their fictions because it’s rather impossible for them to find an ideal one but in the fictional world. Among these male characters, there is Paul Tanner, the woman-hater, and Saul Green in whom one can see understanding to feminism instead of hatred. These male characters share equal importance as female ones in revealing Lessing’s exploration into the feminist theme. Therefore, the thesis is an attempt to analyze these characters from a feminist perspective so as to understand the value of Lessing’s fictions as feminist texts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".