Crisis' as Tool in the Digital Games Industry: Resistance or Command and Conquer?
Bibliographic record
Abstract
From media-driven moral panics to colossal business failures, digital games have historically been rife with crisis, defining the games industry and its practices to a significant degree. More recently, media discourse regarding the very aggressive global economic crisis is host to an ideological game where the form and context of crisis is shaped into a number of disparate and sometimes contradictory conclusions about the current state of digital game development. I will provide an overview pinpointing some of the recent claims made about the digital games industry and relate this discursive context to the ongoing challenges of the peoplewho work within it. Additionally, in an effort to address the title and theme of the Intersections2009 Conference, I wish to highlight the ways in which crisis can be “disruptive”, but also manipulable and productive in ways that reveal both hegemonic industry mandates and opportunities for bottom-up mobilization.
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 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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.045 |
| Scholarly communication | 0.038 | 0.027 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".