Gaming the Publishing Industry: Exploring Diverse Open Scholarship Models in Digital Games Studies
Bibliographic record
Abstract
The emergent field of digital game scholarship has developed along unique communicative lines, illuminating alternative models and diversified potentials for scholarly communication. Following the decline of print-based magazine journalism, the rise of moderated aggregator sites, such as Kotaku, Polygon, and Rock Paper Shotgun has exposed many independent voices to larger audiences. Much of the scholarship cited in current academic work can be found online at sites like Critical Distance (which uses “roundups, roundtables, podcasts, and critical compilations” to encourage dialogue between “developers, critics, educators and enthusiasts”), First Person Scholar, a middle-state publication that combines “the timeliness and succinctness of a blog, while retaining the rigor and context of a conventional journal article” (Hawreliak), highly polished and curated online zines such as Heterotopias, and from quality video bloggers such as Noah Caldwell Gervais and short-form documentary creators such as Gvmers. These heterogeneous alternatives collectively model a publishing plasticity and adaptiveness, establishing a culture of open scholarship practices, inclusive and diverse voices, and a rapid deployment of ideas and perspectives. This paper argues that emergent models of scholarly communication explored by the game studies community include but also moderate the reactive energies of social media and the toxicity of “gamer” culture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.013 | 0.059 |
| Scholarly communication | 0.043 | 0.023 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".