Migrant workers and the imaging of human infrastructure in Chinese contemporary art
Bibliographic record
Abstract
The focus on Beijing’s speed of development and the concomitant fascination with the unchecked destruction of hutongs reveal only part of Beijing’s urban story. If we consider that migrant workers (农民工) are the ‘human infrastructure’ that enables the built infrastructure, then grappling with how contemporary artists depict, exploit, and represent this human infrastructure uncovers many previously overlooked stakeholders. Artists reflect, recombine, and reimagine the figure of the migrant worker. However, such artistic interventions, while a critical avenue for addressing the contested citizenship of urban dwellers, are only one facet of the complex visual field of Beijing. Therefore, in addition to these artists’ works, I discuss other visual elements of Beijing such as the scrawled phone numbers advertising a variety of services for migrant workers on the surfaces of Beijing’s built environment. This unsigned public calligraphic practice is considered alongside the art of globally recognized artists to probe the interconnectedness of urban visual practices, question the targeted constituencies, and examine their reception by a range of urban audiences, revealing the communicative potential of images and text in the urban context and questioning what is at stake for the networks of migrant workers in Beijing that are often invisible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".