Hidden Meaning: Andrew Lang, H. Rider Haggard, Sigmund Freud, and Interpretation
Bibliographic record
Abstract
This essay examines the role Andrew Lang played in the circulation of ideas within and among the fields of anthropology, literature, and psychoanalysis in the late nineteenth and early twentieth centuries. Lang popularized anthropologist Edward Tylor’s theories about myth, and championed them against those of the philologist Max Müller. He provided the occasion for novelist H. Rider Haggard’s engagement with these ideas in the novelShe, which Haggard dedicated to him, and he drew upon these theories of myth in essays that explain the value of Haggard’s novels. Finally, both Haggard’s novel and Lang’s anthropological writing shaped the work of Sigmund Freud. Attention to Lang’s role as a transmitter of the ideas of others across genres and disciplines allows us to see how central the problem of interpretation was to the disciplinary formation of anthropology, literature, and psychoanalysis.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.055 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".