Bibliographic record
Abstract
A gloomy landscape frames a worn yet charismatic stone, carried to the Alberta foothills centuries ago by receding glaciers. Around the rock is a trampled path of dirt and plants that was imprinted onto the land by bison who rubbed against the rock to shed their winter coats. Hidden off to the side is an artist, patiently gathering video footage of this rock and its material history. Thus begins a relationship between a migrant stone, a herd of prairie animals, and an artistic intuition about the importance of watching and listening to the environment around us. Rubbing Rock, 2016. Photo © Moria WhitemanDisplay full sizeA video of this stone is the centrepiece of a recent project by Maria Whiteman that examines questions of geological time and tells (or re-tells) the stories of the lands she encounters. In Whiteman’s work, the stone is juxtaposed with videos of bison, of other environmental sites, and of close shots of grass, ice and water. One might read in this another form of rubbing – not this time the desire to remove a winter coat but rather to contrast the speed of various environmental vitalities. In Whiteman's work the stone is not just a stone but a metaphor – a ‘rubbing rock’ that is also about reconsidering our tactile and kinaesthetic relationships with the landscape. At the same time, the stone is not a metaphor at all – it is actually a stone, and to put poetic elaborations aside is ultimately what grounds the very gaze the poetic intervention seeks to raise. This essay meditates on the use of the rubbing rock in and around Whiteman's work, as a method for thinking about the meeting points of artistic and environmental complexity.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.873 | 0.768 |
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".