Bibliographic record
Abstract
To understand my relationship with my past, I make objects with a sense of urgency to harvest sensations and reveal truths hidden in memory. When I left New Brunswick to emigrate back to the United States, I began to feel a longing to return to Canada. I had found a real sense of home, with deeply personal and profound connections to people and places. I would not truly understand the depth of those connections until I left. As I work to gain perspective on my longing to return to the past, I draw upon Suprematist concepts of creating irrational spaces and giving primacy to feelings over objective visual representation. Both concepts use color and shape to create these irrational spaces and to capture raw emotion. Far from New Brunswick and the people that made me feel welcome, everything I began to make echoed their faces and the landmarks that ground my remembered experiences. To understand the extent and power of memories in my creative process, I considered how to diminish their ability to influence my practice, since everything I made was centered on the past. Could Suprematist strategies offer real ways to distill a memory without diluting the remembered experience, breaking down memory, and discovering truth within the process of longing? If I could not return to living in a comforting past, I would create a window, a portal, a way to dwell in the contentment of that chapter of my life.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".