Young Banyumasan Street Traders as Shapeshifters of Modernity: Refreshment, Production, and the Pursuit of Pranks and Jokes in Jakarta
Bibliographic record
Abstract
Banyumasan Javanese people of Indonesia are often revered as funnier than other Javanese. Ethnographic accounts herein illuminate how young, Banyumasan street traders in Jakarta perform and participate in laughing, joking, and pranking at work. Intersectional analysis reveals the utility of joking and pranking as heuristics to understand the affective dimensions of status, stigmatization, migrating for work, and growing up in Indonesia. The polysemic nature of jokes and pranks reference camaraderie and othering, incongruities and expectations, agency and oppression, as well as intersubjective relations between young men at work. This view of Banyumasan street traders as urban jokers and jesters, producing and consuming humour "from below" for and about each other, departs from previous scholarship on humour in Java, which has focused on how clown characters in staged shadow puppet (wayang kulit) performances have asserted and perpetuated inequalities through a refined-unrefined (halus-kasar) binary whereby those deemed kasar are seen as lacking something. This article, in contrast, asserts the utility of jokes and pranks to refreshing and regenerating understandings of kasar, what it is to be human, and the temporalities, spatialities, and intersubjectivities of boys growing up and working in Indonesia's street economy.
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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 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".