Persistent racialized commodification amidst technological innovation: exceptionalist Filipina bride representations from analog to digital
Bibliographic record
Abstract
Available internet research focus on the linear transition from Web 1.0 to Web 3.0 without realizing their interplay across platforms and interactions with analog and real-world. Such interactions are examined in the foreign bride trade industry’s shifting and stable discursive representations of Filipino women as the industry moved from analog print catalogues to static, unidirectional Web 1.0, multidirectional Web 2.0 and decentralized Web 3.0. Technological changes have transformed digital platforms and information delivery and continued marketable representations of Filipina brides’ racialized exceptionalisms. Shifting from analog to digital platforms has simultaneously disrupted and preserved gendered-racialized hierarchical representations of Filipina brides as simultaneously paradoxical, problematic, and provocative variations in purveying Philippine postcolonial exceptionalism. Critical discourse analyses of sample catalogues and Web 1.0–2.0 websites reveal three persistent exceptionalism variants in Filipina bride representations—extraordinary, comparative, and pragmatic—reproducing racialized-sexualized desires and hierarchies, which endured across technological platforms, simultaneously reproducing and disrupting persistent representations of foreign brides from the Global South, particularly Filipinas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".