Bibliographic record
Abstract
In tackling chronophobia, which Svetlana Boym (2001) defines as the anxiety of deciding how to use our time meaningfully as it depletes, video games become purposeful spaces where we can revisit the things we have lost, or what we anticipate will be lost with time. As such, video games are ideal tools that help us retreat from chronophobia. However, following Boym, I argue that this “does not help us to deal with the future” (2001, p. 351). To revisit or experience what is already “lost” with time through games, players must lose more time and resources in the present to pursue it. This circular use of nostalgia may leave players with chronophobia and in a state of feeling “lost again.” This paper presents three case studies where nostalgic players have “found” something generative for their present and future, rather than feeling “lost again.” This original solution to chronophobia combines Boym’s work, game studies, and nostalgia research, amounting to my contribution of what I call “refractive nostalgia.”
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".