“‘Little bags of remembrance’: du Maurier’s Peter Ibbetson and Victorian Theories of Ancestral Memory”
Bibliographic record
Abstract
In the late-Victorian period considerable speculation that dreams provided access to ancestral memories appeared in both literary and psychological texts. Psychologists and psychical researchers such as Thomas Laycock, Samuel Butler, and F. W. H. Myers, among others, conceptualized memories and dreams as social, rather than solitary, mental functions, capable of making trans-historical connections with other minds. Their theories of ancestral memory effectively challenged the boundaries of subjectivity and the singularity of selfhood by extending identity across generations. One of the most sensational and sustained explorations of ancestral memory was George du Maurier’s 1891 novelPeter Ibbetson,which simultaneously adopted, popularized and expanded this ongoing debate about the nature of memory and the evolutionary scope of the unconscious. This essay traces the various ways in which these discourses on ancestral memory intersect. Both echoing and anticipating writers in the newly emerging memory sciences, Du Maurier’s novel explores the central role of the senses in producing and retrieving involuntary memories and envisions the mechanisms of memory as akin to modern technologies such as photography and phonography. Du Maurier’s account of ancestral memory thereby dramatizes some of the most far-reaching questions in late-Victorian psychology about the relationship between memory, identity, and the material world.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".