Educational Gothic: Interrogating Violent Educations inAustralian, Canadian and United States Fictions 1960-present
Bibliographic record
Abstract
My thesis examines depictions of education within bildungsroman-style Gothic novels and films produced in the United States, Australia, and Canada. I coin the label “Educational Gothic” to define these works. The shared context of settler colonialism is the critical foundation for my comparative analysis. If the bildungsroman has been central to burgeoning settler nations in solidifying a national character and establishing a sense of tradition, I examine its reverse in the Educational Gothic mode. I theorise that the Gothic institution reoccurs as a means of depicting and interrogating violent educational histories within these three nations, particularly when they are not present within official rhetoric. However, the relationship between history and representation is never clear-cut, and creates dynamic instabilities within creative productions. Thus, from 1960 to the present, this thesis charts the emergence and development of the Educational Gothic mode in film and literature, exploring how authors respond to the socio-political climate and examine identity, history and tradition through educational narratives. I delineate two strands of the Educational Gothic mode, Settler and Indigenous, and pay close attention to how these creatives respond to genocidal settler-colonial educational policies towards Indigenous peoples and women’s educational experiences in patriarchal institutions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.026 | 0.020 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".