Imagination, Indigeneity, and Computation: The SIGGRAPH 2018 Art Gallery
Bibliographic record
Abstract
This report addresses the SIGGRAPH 2018 Art Gallery (Vancouver, 2018), its curatorial process, the conceptual guidelines, the methodological approaches, and the sources behind it. The gallery has emphasized a transdisciplinary perspective combining creative and critical projects coming from art, science, and technology. The exhibition was one component of the SIGGRAPH conference, and it was built upon five conceptual nodes, in this text, particular attention is paid to the historical node. The SIGGRAPH Art Gallery is an international show that in 2018 included the work of artists, engineers, and scientists from more than twelve countries participating in the exhibition in situ and from other ten countries participating in the online exhibition. In general terms, the dialog between a diverse set of projects is one of the most compelling aspects of the exposition, the participation of Indigenous artists working with digital media represented one of the most challenging and positive elements of the gallery. The theoretical reflections of Friedrich Kittler about the museums and their relationship with computation and information were a permanent source of inspiration. This text is located halfway between a report and a paper. Therefore, some sections are written in the first person.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".