Don't Let’s Go: Preemptive Grief, a World, and Others
Bibliographic record
Abstract
This paper is written to accompany my MFA thesis exhibition, the work of holding and exiting, which took place April 10 - May 6, 2023 at shell projects, 13 Mansfield Avenue, Toronto, Canada. The exhibition consisted of lumen prints (a sort of photogram gone wrong), sculptures made from found and discarded materials, and elusive feelings that floated through the space. This paper, “Don’t Let’s Go: Preemptive Grief, A World, and Others” is in a symbiotic relationship with the work of holding and exiting. The paper supplements, is informed by, and informs the work. Grieving the climate crisis can feel diffuse, confusing, and hopeless. The works explored this nebulous grieving through affective means. Artworks that are both abject and beautiful were assembled together in shell projects in a way that asked the viewer to be in intimate relation. The works and the paper search for a more-than-human collectivity. Many of the moments, theorists, and beings that have brought these works into fruition find themselves here in the space of this paper: a reflection, an experimentation, a hand reaching out to the audience; this paper cannot quite go it alone, enmeshed and yet different from the work in the gallery.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".