Sibling rivalry, separation, and change in Austen's<i>Sense and Sensibility</i>
Bibliographic record
Abstract
The paper explores a process of growth represented in the interplay of Jane Austen's characterizations of Marianne and Elinor Dashwood in Sense and Sensibility, approaching the text through the lens of psychoanalytic theories on oedipal sibling rivalry, separation, and processes of change. A close reading of Sense and Sensibility tracks Marianne Dashwood's repudiation of any 'second attachment' as the surface of an unconscious fantasy, denying a rival for the mother's love. A psychoanalytic view contrasts Marianne's lack of separation from her mother, her use of denial and projection, and her near death after losing the man she loves, with her older sister Elinor Dashwood's capacities for depression, reflection, and greater acceptance of loss and separation. The narrative portrays Mrs. Dashwood's identification with and idealization of her daughter Marianne, which contribute to her oedipal sibling 'victory'. In the language and structure of the novel, the projections, identifications, aggressions, and separations (conscious and unconscious) of the sisters in the vicissitudes of their adolescent loves and rivalries constitute a process of growth. Austen's novel brings to life, with the vividness and coherence of great literature, forces and fantasies in oedipal sibling rivalries, inspiring renewed attention to their subtle presence in the transference and countertransference of the psychoanalytic process.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".