Bibliographic record
Abstract
Abstract This article takes its starting point from the materialist turn in game studies and, through an examination of the cultural implications of the movement from textual to aural dialogue in digital games, offers as supplement to psychoanalytic approaches already employed in game studies that turn on the embodied experience of gameplay and the materialization of bodies. The embodied practice of subvocalization that accompanies the act of reading is discussed, and several vital tenants of Lacanian psychoanalysis are introduced: the split subject; the Real, Imaginary and Symbolic registers; the mirror stage and its acoustic counterpart; imaginary and symbolic identification; fantasy; and the object voice. Lacanian psychoanalysis provides a critical frame revealing that aural representation of dialogue enables players to better identify as game characters, a narcissistic investment in the ideal subject of the game. Textual representation, reading and subvocalization of dialogue, on the other hand, better enable identification with the game itself, the very system that demands a certain subject. While no guarantee, this is a condition of possibility of confronting the underlying structure of fantasy that organizes all digital games, regardless of their thematic, mechanic and narrative particularities, and thus a condition of possibility for players to recognize how their gameplay is implicated in consumer capitalism. The article not only argues for the importance of games criticism considering this oft-overlooked aspect, but it also points to material experiences that are generalizable across populations of players.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".