Memories of the Future: Speculative Cold War Histories in Yosep Anggi Noen's The Science of Fictions and Daniel Hui's Snakeskin
Bibliographic record
Abstract
In 1965 Indonesia, the CIA helped Suharto spread false reports on a coup plot by the PKI (Partai Komunis Indonesia), contributing to an anti-communist purge; that same year, a farmer chances upon a film shoot by a foreign crew of a fake moon landing and has his tongue cut off. The latter is a speculative invention of Yosep Anggi Noen’s fiction film, The Science of Fictions (2019). In Daniel Hui’s hybrid speculative fiction / documentary Snakeskin (2014), set in the year 2066 in Singapore, references to the 1950s Chinese leftist movements form part of the film’s excavation of national myths and half-forgotten memories. Noen’s imagined past and Hui’s speculative future meet in Cold War secrecies that periodically haunt the cinema of the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".