Bibliographic record
Abstract
This chapter explores the creation and development of horror through the tropes of silence and excess, focusing on 1990s novels by Linda Hogan (Chickasaw), Eden Robinson (Haisla/Heiltsuk) and Alexis Wright (Waanyi). The chapter demonstrates how silence and excess exhibit the underlying principles and stylistic advancements of the broader genre, which the author coins as Indigenous Educational Gothic. Indigenous Educational Gothic responds to the horrifying historical realities of Indigenous schooling in the US, Canada and Australia, and these narratives are often told through the eyes of young female protagonists. Silence and excess expand the parameters of horror in tandem with and informed by Indigenous resurgence, “grounded normativity,” and authors’ specific storytelling traditions. Ultimately, the chapter examines how literature can ethically and profoundly engage with dark historical narratives and it initiates a dialogue on Indigenous horror within mainstream literature and how this responds to implications surrounding readership and positionality.
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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 | 0.000 | 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) | 0.001 | 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".