Bibliographic record
Abstract
This paper presents a systematic analysis of concepts of information found in the visual art of the Vancouver-based conceptual company N.E. Thing Co. Ltd. The company’s evolving representations of “Sensitivity Information” are resolved into three distinct overlapping phases that correspond with co-president Iain Baxter’s deepening engagement with Marshall McLuhan’s critical information theory during the turbulent years of 1964 to 1971. The artist’s creative dialogue with information science is situated against the backdrop of an emerging information society in Canada, McLuhan’s discourse on the informationalization of the body and subjectivity, and an intensifying pedagogical crisis at Simon Fraser University’s Centre for Communication and the Arts,where Baxter taught alongside composer R. Murray Schafer,in the period leading up to, and immediately following, the firing of the university’s first president. A comparison of the information art of N.E. Thing Co. Ltd. with Schafer’s informatic writings reveals that the company’s articulation of Sensitivity Information constitutes what we would now recognize as a proto-deconstructionist destabilization of signal/noise binaries derived from Baxter’s fusion of McLuhan with Alan Watts’s popularization of Zen philosophy.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".