Object loss, renewed mourning, and psychic change in Jane Austen's<i>Persuasion</i>
Bibliographic record
Abstract
Austen's extraordinary realism in depicting the dynamic internal processes which follow on the heroine's loss in Persuasion becomes clear in the light of a psychoanalytic understanding of mourning. Persuasion dramatizes the effects of a mother's death in adolescence as these come into play at the time of the heroine's separation from her fiancée and her later mourning. The thesis of this paper is that, despite falling in love with the brilliant hero, an unfinished mourning and an unconscious identification with her dead mother helped to persuade the heroine Anne Elliot to break her engagement, to create a 'final parting' as her mother had done to her in dying. The heroine's internal monologues show that she has projected some of the darker feelings of mourning, her anger and resentment, on to the hero and that she reopens a complex mourning process, partly through the displacement of affect, showing how traumatic effects of loss can be worked through in deferred action, effecting positive psychic change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".