Bibliographic record
Abstract
LEARNING FROM ICE is a multi-year artistic project researching the ways in which different knowledge practices are investigating and responding to changes taking place within the Circumpolar North under the accelerated conditions of global warming. It is comprised of a series of documentary films exploring ice core science, sea ice, and glaciers, as well as a field school in collaboration with Nunavut Arctic College around the climate change concerns of Inuit youth, and an ice law forum on the right to be cold at the University of Toronto. Ice Cores (REF Submission). To-date this research has unfolded with filmed interviews and site visits to geochemistry labs and national ice core repositories in Canada and the US as well as fieldwork in the Columbia Icefields. From industrial black carbon deposits, atmospheric nuclear testing, to greenhouse gases resulting from the combustion of fossil fuels, glacial ice sheets have been systematically ‘recording’ evidence of these planetary processes. This archival condition has enabled me to link the worlds of Earth Science with the Humanities both of which share an interest in the material records of the past. In addition to answering scientific questions, ice cores are increasingly being used to track societal and cultural changes such as the effectiveness of environmental policies, epidemiological data linked the Black Death and even the imperial expansion of the Romans, which corresponds to lead pollution within the ice matrix. All of this is explored within the documentary. My primary objectives in making the film were to translate and narrate the complexities of ice core science to non-specialists in various public forums (institutions, schools, galleries). Commissioned by the Toronto Biennial of Art and supported by the Office for Contemporary Art Norway, the film is actively being exhibited and screened in Canada, the US and Scandinavia.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".