‘Everything happens in Laura’s dorm room’: Carmilla webseries reimagines LeFanu’s novella
Bibliographic record
Abstract
The Canadian webseries Carmilla (2014-) is both an adaptation of Sheridan LeFanu’s 1872 novella and—with three 36-episode seasons (and a season zero), inter-seasonal content, over 35 million YouTube views, and a movie—a successful transmedia production in its own right. This paper examines Carmilla and its operation as a digital reimagining for the twenty-first century, arguing that its success demonstrates the flexibility of Gothic tropes, characters and narratives. The updated setting (a university campus) and mode (straight to camera pieces ‘filmed’ on journalism student Laura’s webcam) make the characters and their situation familiar for viewers who might never have read ‘Carmilla’ by drawing on teen genres and found footage horror. Adapting the novella as a single frame vlog-style webseries involves negotiation of the ‘original’, not least, as creator Hall notes, that ‘everything happens in Laura’s dorm room’ (in O’Reagan 2014). All the regular characters are female and ‘somewhere on the LGBT spectrum’ (Hall in O’Reagan 2014), a call back to the way LeFanu’s novella has frequently been adapted or used as inspiration for lesbian vampire stories. This also helps the webseries find its audience: despite entrenched assumptions about horror and who it is ‘for’, women have long been horror fans and Carmilla has a loyal fan following of ‘creampuffs.’ By analysing the various creative decisions in adapting ‘Carmilla,’ this paper argues that the webseries is a key site challenging outmoded assumptions about Gothic, horror, adaptation and remakes, target audience and use of spectacle and gore.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".