MétaCan
Menu
Back to cohort
Record W2601916917 · doi:10.1080/15295036.2017.1304648

When paratexts become texts: de-centering the game-as-text

2017· article· en· W2601916917 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCritical Studies in Media Communication · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSociologyFandomParatextMedia studiesAdvertisingLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Most academic research on and discussions about paratexts define them as texts or artifacts that surround a central text, lending that central text meaning, framing and shaping how we understand it. Researchers who study games and game culture have examined how materials such as walkthroughs, game guides, and Let's Play videos function as paratexts to shape how we understand what a particular videogame might be like and how best to play it. Yet sometimes texts become paratexts themselves when the object of study shifts. In this short essay I explore situations where the (seemingly) central object becomes de-centered, where the game becomes the paratext for other texts. These cases demonstrate the danger in "fixing" some texts as central and others as peripheral. By discussing the worlds of game modding and professional streamers on Twitch.tv, I argue for flexibility in when a text might become a paratext and vice versa.

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.

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)0.001
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.124
GPT teacher head0.446
Teacher spread0.321 · 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