(Her)itage: Literary Tourism and the Popular Legacies of Louisa May Alcott, L.M. Montgomery, and Beatrix Potter.
Bibliographic record
Abstract
This dissertation theorizes tourist engagements with famous literary women through an examination of the popular legacies of Louisa May Alcott, L.M. Montgomery, and Beatrix Potter. Little Women (1868-1869, American), Anne of Green Gables (1908, Canadian), and The Tale of Peter Rabbit (1902, English) have been translated into 30+ languages and continue to inspire admiration, adaptation, and tourist pilgrimage. Three literary house museums are analyzed, using interdisciplinary methods including close readings of primary materials, personal interviews, and field observations. The term (her)itage is proposed to describe a range of contemporary emulations of historical feminine subjectivities that furnish comfort, escape, and inspiration to adult women who creatively annotate their own identities with ideas, values, and even clothing referencing these writers and their works. Each case study focuses on a different community of interpreters who recall, rehearse, and reimagine the popular legacies of these writers. Chapter One examines the call to emulate Louisa May Alcott by guides at Orchard House (Concord, MA). Chapter Two analyzes tourists' recreation of Anne's arrival at 'Green Gables' (Cavendish, PE). Chapter Three compares interpretations of Beatrix Potter's legacy by the makers of the romantic drama Miss Potter (2006) and staff at Hill Top Farm (Near Sawrey, UK) and the emulations each might inspire. Chapter Four examines the museum stores of each site, where institutional and consumer identities entwine, their points of intersection expressed in the meanings of goods sold and purchased. Contributing to the fields of literary tourism studies, postfeminist popular culture studies, and museum/heritage studies, this examination of the extraliterary legacies of Alcott, Montgomery, and Potter asks what aspects of historical feminine subjectivities 21st century women are drawn to emulate and what these practices reveal about the longings and anxieties of contemporary women, while speaking to larger questions about the synergy between historic sites and popular culture productions and the blending of personal histories and cultural histories that motivate visits to museums and historic sites.
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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 | 0.001 |
| 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 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".