Bibliographic record
Abstract
Vossen outlines her experiences in the game studies community from 2012 to 2022 focusing on the ludology vs narratology debate's impact on herself and other game scholars. She argues that these “debates” alienated many scholars looking to become part of the field by outlining a “right” and “wrong” way to study games. Vossen discusses how essays like “Genre Trouble” and the larger ludology vs narratology debate made her feel unwelcome as someone with a PhD in English examining issues of culture and gender in games. In the tradition of historiography, Vossen examines how the histories of the debate have been recorded, reinterpreted, perceived, and felt by multiple generations of scholars. Vossen argues that the debate is the story of how the work of a few men who used similar theories and methods were canonized. Consequently, games-related work that uses other methodologies, often favoured by women and queer people, are marginalized and pushed to the fringes of the field. Vossen concludes that it is only by continuously discussing and addressing the failures of these early debates and their impact on game studies that we can undo the harm they have caused.
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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.038 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".