Bibliographic record
Abstract
In 2018, professor Yevgeniya Kaganovich launched the project Divergent Fates, a project diving into Kaganovich’s longstanding research interests in materiality, particularly her research into reused plastic, polyurethanes, and rubber. The current portion of this project, Tree Inuit's Chair, focuses on using reclaimed materials like paper, cardboard, plywood, and chipboard, and using these new materials to develop a new body of work. This work addresses issues of material agency, sustainability, consumption, and human impact on the environment. Currently, our research team is working on reverse-engineering trees from chairs and making trees as they might be remembered by paper. In order to do so, we are tending to grafted trees at Lynden Sculpture Garden as well as crafting artificial wood out of layered newspaper held together by wood glue. At Lynden, we landscape and provide upkeep to the sculpted trees, as well as assist in the process of grafting and arranging the branches to form desired shapes. When we aren’t at Lynden, we work in Yevgeniya’s studio, building the newspaper logs layer by layer. In addition, previous portions of these newspaper logs are being cut and arranged to imitate the shadow of a chair. Recently, the work has been exploring how to finish these pieces and methods of attachment. So far, the most successful method we have discovered is simply coating the sections in wood glue, which is later sanded off to reveal the “grain” of the “wood”. As for the methods used to attach the pieces together, we are experimenting with fiber techniques such as sewing and crocheting. We are expecting the Lynden trees to continue growing over multiple years into chair and shelter shapes, as well as completing the chair shadow made from its artificial counterpart. These final results will convey ideas about sustainability, memory, and the importance of materials.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.047 | 0.011 |
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".