Bibliographic record
Abstract
Abstract How do we experience time in an era of digital, networked communication? What if time becomes an algorithmic database that provides random accesses and entries to different moments in the past or the future? This essay focuses on a 2009 Chinese film, Lee’s Adventures , to explore the intricate interrelations between digital media and the representability of time. The film features a young urban professional named Lee, who is frenetically obsessed with playing a video game in order to travel to a different time. The film constantly highlights a suffocating, efficiency-centered corporate time that Lee has to endure as a petty clerk in a cold, glass-surfaced office building. The video game is taken by him as a “time machine” to escape from the homogenous, hollow present. Yet ironically, although the video game becomes a vehicle for Lee to travel to a different time, his access is structured by the algorithmic database of the game. Developing from an analysis of the algorithmic aesthetic of the film, I argue that the ideological functions of the index of analog cinema are now taken over by algorithm, which provides a new structure to manage the contingent. Tracing this tension between the determinacy and the play inherent in the standardization of time since the early modern period, this essay in the end asks how digital media redefine cinema’s role in restructuring time.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".