Bibliographic record
Abstract
While Singapore is not an oil-producing nation, it occupies an important role as one of the largest refinery hubs and the world’s busiest bunkering port for tankers and container ships. However, existing scholarship on petroculture has largely bypassed Singapore in its focus on direct representations of oil centred on upstream producers such as the United States, Canada and countries in the Middle East. This article traces the emergence and eventual disappearance of oil in Singapore’s visual culture through two moving images made separately in the late 1950s and the early 2000s. Comparing L. Krishnan’s film Orang Minyak (), which headlined a series of local films that featured the urban legend of the orang minyak (‘oily man’), with Tan Pin Pin’s documentary video 80km/h () allows us to consider the sudden eruption and eventual disappearance of oil in Singapore’s visual culture, set against the historical developments of Singapore’s oil industry. Of interest here is an attention towards both direct and indirect representations of oil. The article ends with a formal analysis of Tan’s 80km/h, through which I argue for a critical petro-aesthetics particular to Singapore itself, in thinking through its role as an important middleman in the global supply chain of petroleum and petrochemicals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".