Wise Women and Witches: Women and the Supernatural in Nineteenth-century Scottish Writing
Bibliographic record
Abstract
This dissertation foregrounds the relationship between women and the supernatural in nineteenth-century Scottish writing. Writers of both fictive and non-fictive texts used the supernatural to explore ideologies about gender. I argue that many used the supernatural to explore issues of power, agency, and consent, particularly with regards to women’s rights, roles, and responsibilities in nineteenth-century Scotland. Authors and collectors ultimately suggested that women held power in their speech, craft, and bodies – though they debated the extent of those powers. \nI argue that writers and collectors used the supernatural to explore both seventeenth- and nineteenth-century ideologies about gender and suggest that both were concerned with issues of cultural identity, agency, and consent. Renewing their focus on the witch-hunts, nineteenth-century writers centered both women and the supernatural at the heart of this discussion. Nineteenth-century Scots sought to distance themselves from their “superstitious” ancestors while also celebrating these unique and “authentic” representations of Scottish culture. \nI explore two case studies of ethnographic writing by women, suggesting that women writers often had to navigate criticism focused on their gender rather than the merits of their work. Women writers had to perform femininity carefully – creating their authority while maintaining their reputations. Women contributed to the development of the genre, and their writing reflects several shared concerns: (super)natural landscapes, religion and “superstition.” and marriage. \nExamining representations of women as storytellers reveals that many collectors or authors who constructed these representations were negotiating who should have power over which stories were told and how stories were received. Editors, collectors, translators, and authors emphasize or deemphasize their presence to control cultural narratives – including narratives about gender. \nI argue that collectors and writers turned to nineteenth-century texts which used female figurations of the supernatural to explore how women’s powers were embodied. (Re)contextualizing nineteenth-century writing that claimed to document beliefs about the supernatural alongside fictive material shows how these texts were used to legitimise or de-legitimise movements and arguments, supporting or detracting from value systems. These texts suggest that women held power in their bodies, crafts, and speech, but that they had to negotiate use of these powers carefully.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".