Bibliographic record
Abstract
Over the past two decades, machine vision and artificial intelligence (AI) have become integrated into many aspects of daily life, and with machines generating images for other machines, humans are no longer at the centre of the image world. To explore the implications of the age of machine vision, this article focuses on a selection of work by American artist Trevor Paglen made between 2008 and 2018. It examines the significance of the US-Mexico border region as a site of his investigations and the role of abstraction in repositioning machine vision within the human-centred visual language of art. The article shows how Paglen’s body of work turns toward the nonhuman to consider what it means to make images when representation is no longer primarily a site for the construction of meaning by humans but is also a field of data for analysis by machines. It explores how Paglen conveys the interplay between different models of vision with an aesthetic sensibility that develops from the history of photography but signals the breakdown of representational photography. His work highlights the contested space of the US-Mexico border region as a key site in a surveillance infrastructure that depends on a new regime of vision and offers a space to reflect on what it means to live in a world where most images are now made by and for machines.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".