Bibliographic record
Abstract
How does critical AI studies itself travel, and what happens when it arrives elsewhere? This commentary reflects on the global circulation of critique from the standpoint of East Asia, where layered histories of modernization, technological aspiration, and cultural traditions generate alternative ways of knowing, imagining, and critiquing AI beyond South–North frameworks. Writing from my position as a graduate student from East Asia, now trained in a North American institution, I introduce three analytical vectors— tangle, transplant , and transmute —all of which emerge dialogically through my research on AI innovation in East Asia and through engagement with Asian media scholarship addressing similar concerns. Through vignettes from this research journey, I suggest that these themes illuminate how dominant critical vocabularies encounter local imaginaries, on-the-ground frictions, socio-cultural histories, and divergent ethical orientations. Rather than proposing a unified Asian critical theory of AI, I offer “traveling AI” as a reflexive praxis that centers relational co-constitution, situated reworking, and philosophical reorientation, while remaining attuned to epistemic tensions and power differentials. In dialogue with broader de-westernizing projects, this paper suggests that East Asia can contribute to reimagining critique not as theory from the center or the periphery, but as an ongoing praxis of troubling with in-betweenness .
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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 | 0.024 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.043 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".