The candidate : a novella and examination of Australian Gothic crime fiction
Bibliographic record
Abstract
Australia is a land without haunted castles or subterranean corridors, without ancient graveyards or decaying monasteries, a land whose climate is rarely gloomy. Yet, the literary landscape is splattered with shades of the Gothic genre. This Gothic heritage is especially evident within elements of nineteenth century Australian sensation fiction. Australian crime fiction in the twentieth century, in keeping with this lineage, repeatedly employs elements of the Gothic, adapting and appropriating these conventions for literary effect. I believe that a ‘melange’ of historical Gothic crime traditions could produce an exciting new mode of Gothic crime writing in the Australian context. As such, I have written a contemporary literary experiment in a Gothic crime ‘hybrid’ style: this novella forms my creative practice. The accompanying exegesis is a critical study of a selection of Australian literary works that exhibit the characteristics of both Gothic and crime genres. Through an analysis of these creative works, this study argues that the interlacing of Gothic traditions with crime writing conventions has been a noteworthy practice in Australian fiction during both the nineteenth and twentieth centuries and these literary tropes are interwoven in the writing of ‘The Candidate’, a Gothic crime novella.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".