Making Sense in Ludic Worlds. The Idealization of Immersive Postures in Movies and Video Games
Bibliographic record
Abstract
In the ongoing efforts to theorize the interactive experience proposed by video games, it is common to make a distinction between fictional elements and the gameplay in itself. E. Adams distinguished between tactical, strategic and fictional immersions. In Half-Real, J. Juul has notoriously declared that video games encompass two things: fictional worlds and real rules. Many approaches stress the distinct nature of the immersive experience in games on account of their participatory nature. By contrast, M. Csikszentmihalyi’s model of flow – a common foundation to discuss immersion in sports and games – has been applied without any modifications to art appreciation, an “activity” that many would argue doesn’t propose clear goals and retroactions. Is there any common ground between games and fictional forms that can help us understand the cultural magnitude achieved by their synthesis through the video game medium? Building on current doctoral research and on Jean-Marie Schaeffer’s effort to theorize our involvement with digital worlds as a continuation of the fictional immersion experienced in other media, this contribution seeks to evaluate the relevance of a general framework to discuss immersion. The optimization of experience in both video games and fiction films, and the various strategies that seek to shape an ideal immersive posture for us to inhabit, serves as foundation for the discussion.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.040 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".