“The Ending Has Not Yet Been Written”: How Myst, S., and Other Transmedia Creations Pay Tribute to the Analog in a Digital World
Bibliographic record
Abstract
With the arrival of new media comes the age-old anxiety that more traditional media – namely tangible forms like the print book – will be displaced, abandoned, or forgotten entirely. However, the book continues to be incorporated into sculptures that surround libraries and clothing and tote bags that we purchase, signifying that the traditional codex holds a lasting place in our hearts. This paper argues that newer forms of media can function as outlets for our love of physical manuscripts and can renew our infatuation with them rather than threaten to replace them. Digital media like Rand and Robyn Miller’s 1993 computer game Myst, as well as transmedia like J.J. Abrams’ and Doug Dorst’s digitally-supplemented novel S. use immersive techniques to recreate the feeling of “losing ourselves” in a good book, intensifying a desire to explore and discover in the physical world and the virtual world alike. Moreover, this paper utilizes Jay David Botler’s and Richard Grusin’s theory of remediation to suggest that transmedia has the potential to inform new engagement with the physical world, as is evident in trends like escape rooms and geocaching.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".