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Record W2805518549

Twenty-first century television horror's terrible places: a roundtable discussion

2017· article· en· W2805518549 on OpenAlexaboutno aff
Lorna Jowett, Stacey Abbott, Rebecca Janicker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTheme (computing)NarrativeStorytellingFranchiseMovie theaterTelevision seriesHistoryArtMedia studiesVisual artsAdvertisingLiteratureSociologyComputer scienceWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

The ongoing boom in TV Horror coincides not only with the expansion of TV markets but also with new innovations in delivery platforms, viewing habits, and modes of engagement. In this round table the panellists will initially analyse how three particular examples of TV horror find their home in this new TV landscape by innovating in terms of form and format, before moving on to more general debate about how horror positions itself on television and what might count as TV horror in 2017. In October 2011, a program entitled simply American Horror Story hit the small screen for the first time. Rebecca Janicker sketches out how series co-creators Ryan Murphy and Brad Falchuk were quick to develop a fecund concept into a horror anthology franchise which now comprises six series dedicated to a diverse array of gruesome and shocking scenarios, situated in particular temporal and geographic settings. AHS appeared in what Jason Mittell has described as a growth era for TV and the show’s novel approach to storytelling has served to distinguish it from other horror narratives. AHS combines the mini-series model of a finite run of episodes that culminates in final narrative closure with the traditional TV model of weekly instalments on FX. Unity of theme renders each season a distinct ‘text’ and it is this feature, along with the celebrated recycling of actors, that marks American Horror Story out as a new format for TV horror: an anthology franchise rather than an anthology series. On-Demand streaming service Netflix has fast become a prime site of ‘television’ production and distribution globally, while simultaneously calling into question the nature of TV. Stacey Abbott will identify the ways in which Netflix production Santa Clarita Diet (2017-), an innovative horror-comedy hybrid, transforms and, arguably, undercuts the ubiquitous zombie sub-genre by adapting it to the suburban sit-com format. The series follows Sheila (Drew Barrymore) and Joel Hammond (Tim Olyphant), suburban realtors based in Santa Clarita, California, as they discover that Sheila has become a zombie, with the requisite taste for flesh and blood, as well as an increasingly voracious sex drive but also the desire to maintain her ‘happy’ home. While this is a call out to Golden Age supernatural sitcoms such as The Munsters (1964-66) and The Addams Family (1964-66), its place on Netflix allows it to function fully as horror, and the series' repeated scenes of graphic body horror tip gross out comedy into full on horror. This Netflix production offers an engaging opportunity to consider how the changing nature of television and a burgeoning landscape of new streaming platforms impact upon the presence and evolution of horror for the small screen. Moving even further away from the traditional television series format, Lorna Jowett briefly examines key characteristics of Canadian web series Carmilla (2014-). With three 36-episode seasons (and a season zero), inter-seasonal content, over 35 million YouTube views, and a movie on the horizon, Carmilla is a highly successful digital adaptation or reimagining of Sheridan Le Fanu's 1872 novella that demonstrates the flexibility of horror or Gothic tropes. The web series also taps into new technologies and new media forms by presenting the narrative as the vlog of university student Laura, and much of its innovation comes from the restrictions of this format. Carmilla unsettles both the notion of the horror monster and the genre convention of the terrible place by repositioning the story and the characters: the contemporary campus culture of the updated setting inflects how the characters--who are almost all non-binary/non-heteronormative in this version--operate within the story. Having offered a snapshot of the changing face of TV horror through these case studies, the panel will expand the debate and address a range of questions, seeking to unpack how a new broadcast landscape affords new 'terrible places' for horror to evolve. Has television become the new home of innovation in horror? What opportunities does television and its various forms and formats offer to horror? Given the centrality of space to horror/Gothic, in what ways does TV horror negotiate this geography? How does the emphasis on characterization found in so-called 'quality TV' impact on horror and its spaces? How do the chosen case studies take advantage of being TV Horror now that television frequently overflows its boundaries to become 'spreadable media'? How do audiences and fans enter and experience TV horror's terrible places? In what ways is the traditional gendering of the horror genre challenged by the boom in horror on TV, historically a domestic media? In debating these questions and areas the round table aims to, at least partly, map the new spaces, terrible and otherwise, that TV Horror has invaded, infected or (re)imagined.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0190.011
Scholarly communication0.0160.010
Open science0.0030.010
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0200.002

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.

Opus teacher head0.020
GPT teacher head0.310
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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