MétaCan
Menu
Retour à la cohorte
Enregistrement W7054513549

Alone Together - Convergence Culture and the Slender Man Phenomenon

2023· dissertation· en· W7054513549 sur OpenAlexfundno aff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2023
Typedissertation
Langueen
DomainePhysics and Astronomy
ThématiqueAdvanced Frequency and Time Standards
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of Waterloo
Mots-clésPhenomenonMonsterAmateurFantasyHoaxHarassmentOrder (exchange)StorytellingPoliticsHarm
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This project engages in a close examination of the Slender Man phenomenon, an online practice in which a community of pseudonymous enthusiasts share scary stories featuring a faceless, long-limbed, humanoid monster in a black business suit. The stories take various forms, including text-based narrative, amateur video, doctored images, and games. They are presented with an affectation of folklore, and treat the accounts as true testimonies of encounters they, or others they know, have allegedly had with Slender Man. This is a self-conscious effort on the part of its creators to manifest Slender Man as a real-life legend. Resulting from this effort, several individuals have carried out acts of real-world violence in the name of Slender Man, or with some connection to him. In response to these acts, and the ensuing moral panic, members of the community defensively stated that it was the responsibility of their readers to be able to know the difference between fantasy and reality. Yet, as this dissertation demonstrates, the Slender Man phenomenon itself is predicated on using digital media to blur this distinction.
\nThrough readings of Slender Man in various media forms, this dissertation shows how it blends horror aesthetics with the online cultures of trolling—in which individuals intentionally misrepresent themselves in order to mislead and antagonize others, allegedly for the lulz—that is, for the laughs, pranking or joking. Trolling has however produced many serious consequences, from individuals targeted for harassment to bad-faith political movements that disrupt existing institutional functions more broadly. In its origins, trolling began as apocryphal storytelling designed to mislead others into believing they were true and expose the ignorance of newbies. Notably, the sites in which this occurred evolved to become the fora from which the similarly apocryphal stories in the Slender Man text community originate, such as 4Chan. These same pseudonymous fora have acted as safe havens for bad actors that have gone on to become notorious for their promotion of real-world violence, from Erik Minassian’s violence in the name of the incel community to Elliot Rodger’s misogynistic manifesto posed to 4Chan.
\nIn short, this dissertation argues that Slender Man texts act as a canary in a coal mine, and that the mechanics of online horror communities lay bare the underlying strategies of trolling or post-truth internet culture more broadly. I undertake a close aesthetic and ideological examination of Slender Man in image, text, video and game, to offer a portrait of the community that shares them. The stories offer a glimpse into the anxieties, tensions, and alienation experienced in life online as a result of hypermediacy, premediacy, and anonymity. While much has been written regarding the potential for collaboration online and the possibilities for grassroots organization and community-building, the positive ends this convergence culture offers are offset to some extent by the kinds of anxieties emerging from a disaffected and alienated community. Ultimately, this project offers an account of the evolving relationship between interactive fiction, trolling, and political disaffection, a media ecology that is becoming ever more urgent to understand in twenty-first century society.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,861
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,007
Tête enseignante GPT0,210
Écart entre enseignants0,203 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueUWSpace (University of Waterloo)Même sujetAdvanced Frequency and Time StandardsTravaux en français237 207