Tecnoflâneurs y faquires: El arte al otro lado de la brecha digital
Bibliographic record
Abstract
In this article we look at four international artists whose art has its origin in common civilian life and its concerns. The story of this art should be re-written in terms of a historiography of the average underprivileged common person, who does not reap the benefits of a discriminatory economy. Artists discussed here, namely, Daniel Cruz (Chile), Gilbert Prado (Brazil), Kausik Mukhopadhyay (India) and Probir Gupta (India) have been creating art on the impoverished side of the digital innovation divide, in their own niche and horizons of belief. Discarded gadgets, scraps, broken circuits or sensors, microphones and other junk are incorporated to create fragile but impactful installations. Junk animism and low-fi artificial intelligence often inform their work. Such artists do not inhabit traditionally known borders of nation, class or identity but only a space across fault lines which divide and exacerbate human society from within. Cruz’ project titled Surfonic exists on the margins of internet gateways. Mukhopadhyay uses scrap media for his installations. They exploit so much technology as just to animate their art. This commitment to voluntary defeatism upends a culture of spectacle. The artist is like a flaneur or technological fakir, quintessentializing human experience against the greed and pretensions of a global market of art.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".