Surface Tension : Matthew Buckingham, Nina Canell & Robin Watkins, Youngmi Chun, Kelly Jazvac, Sreshta Rit Premnath, Jimmy Robert, Mark Soo
Bibliographic record
Abstract
Over the past decade, digital media has transformed how we take pictures, how existing images circulate in the world and how histories are written. While many artists have questioned where to locate images in our contemporary context, their ongoing presence in museums and galleries underscores that—alongside more dematerialized forms—images remain with us physically.Surface Tension presents recent works by Canadian and international artists that readily engage with this persistent materiality. In particular, the works here share an interest in the surface of the image and its susceptibility to intervention. From the rough texture of newsprint to the semi-gloss of a snapshot to the sheen of advertising vinyl, it is surface that establishes our encounter with every image. When that surface is unsettled—as happens to the works on view in this exhibition—new relations between images and objects emerge.Moving beyond the traditional framework of photography, the meanings of the works in Surface Tension are not limited to the subjects of individual pictures. Instead, meaning is made through strategies of assemblage, with images expanded, illuminated or even undone as they assume material form.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.149 | 0.040 |
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".