MétaCan
Menu
Back to cohort
Record W4415045503 · doi:10.7202/1118964ar

Screen Time

2024· article· en· W4415045503 on OpenAlexvenueno aff
Anitra Lourie

Bibliographic record

VenueSens public · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicNarrative Theory and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeNarratologyAppropriationChronotopeReflexivityNarrative networkNarrative inquiryNarrative criticismIdentity (music)Narrative structure

Abstract

fetched live from OpenAlex

This article examines contemporary artists’ appropriation of social media platforms to explore new narrative forms. In addition to the aesthetic and thematic qualities of these works, artists’ social media narratives reveal pragmatic and discursive qualities, notably unique space-time configurations. Artists such as Molly Soda, Amalia Ulman, Martine Gutierrez, or even the design team Brud’s Instagram CGI character Lil Miquela, highlight the temporal and spatial narrative dimensions of social media designs and the ways these platforms facilitate, guide, and frame self-representation and storytelling. This article will explore the ways in which narrative elements are both integrated into the design of social media platforms and reappropriated artistically for critical or reflexive use, (re)constructing the intentions and potentials of both the technology and the narrative concepts. This reflection draws on literary discourse theory as well as digital narratology and socio-linguistics, specifically employing French philosopher Paul Ricœur’s concept of narrative identity and emplotment as well as Russian structuralist Mikhail Bakhtin’s theory of chronotope (Greek for “time-space”). Through this analysis, we interpret artistic social media practices as self-narratives that employ medium-specific temporal configurations and challenge the traditional narrative framework.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0320.007

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 head0.031
GPT teacher head0.226
Teacher spread0.195 · 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; both teacher heads agree on what is shown here.

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueSens publicSame topicNarrative Theory and AnalysisFrench-language works237,207