Cyber Siren: What <i>Mami Wata</i> reveals about the Internet and Chinese presence in Kinshasa
Bibliographic record
Abstract
In 2012, images of a mystical mermaid known locally as Mami Wata circulated on the Internet and via people's mobile phones, sparking rumours that Chinese labourers had captured her as they were installing underwater fibreoptic cables. Appearing as a grotesque sea-creature with a gnarled, shrivelled body, this new image of Mami Wata challenges older, popular depictions of her as a beautiful maiden. Further, in her deformed body, Mami Wata reveals new tensions arising from promises of wealth and modernisation promoted by both Chinese and Congolese governments. Accounts of rumours/urban legends and metaphors of contagion animate larger contemporary discussions concerning development projects, “otherness” and the influence of the Internet and mobile phone technology on production of popular African culture. The female siren, Mami Wata, is a recurring motif in Kinshasa's collective urban imaginary. Historically she has been an expression of modernity and hybridity through visual representation in popular painting, sculpture and television serials. Now Mami Wata appears in the digital world. In this article, in addition to analysing the ways in which contemporary technology mediates this archetypal figure, I draw on notions of otherness, recent historical, political and economic changes in the Democratic Republic of Congo to analyse the ways they inform the particular shape and meaning that Mami Wata takes when transformed into the digital domain.
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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 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".