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Record W4412619875 · doi:10.53288/0441.1.26

Navigating a Complex Space

2025· book-chapter· en· W4412619875 on OpenAlexaff
betsy brey

Bibliographic record

VenuePunctum Books · 2025
Typebook-chapter
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSpace (punctuation)Computer scienceOperating system

Abstract

fetched live from OpenAlex

Henry Jenkins is Provost Professor of Communication, Journalism, Cinematic Arts and Education at the University of Southern California—or as he likes to put it, “Professor of Miscellaneous Studies” or just “media scholar at USC.” A prolific author, Jenkins has author and editor more than 20 books including Textual Poachers, Convergence Culture: Where Old and New Media Collide, Fans, Gamers, Bloggers, and most recently, Comics and Stuff in addition to hundreds of articles on a variety all sorts of pop culture and digital culture subjects, looking at fan communities, comics, the internet, film, and much more, including a very active period working on video games, writing well-known and well-cited pieces such as “Games as Narrative Architecture,” “Games: The New Lively Art,” and “Popular Culture as Politics, Politics as Popular Culture.” Jenkins, however, no longer considers himself a game scholar—an interesting distinction. This raises the question, “what does it mean to do game studies?,” in this interview Jenkins and I discuss the roots of game studies as an area of study and its development into a unique and sometimes baffling field. Reflecting on his journey through the development of the field, Jenkins describes the spaces of game studies as they were shifting from an area closely aligned with industry and popular culture towards a more isolated academic field pushed away from development spaces. From the sparking of his interest in play and spatial properties of games, to his defense of video games in front of the US Senate, to his scapegoated position in the ludology/narratology debate, Jenkins has seen the field from many perspectives—most importantly, he sees the field from outside of it now.

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)0.008
metaresearch head score (Gemma)0.009
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: Other
Teacher disagreement score0.030
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0250.063
Scholarly communication0.0300.046
Open science0.0030.024
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0230.004

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 head0.039
GPT teacher head0.309
Teacher spread0.270 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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