Fleet: Nuances of Time and Ephemera
Bibliographic record
Abstract
The following MFA thesis is an investigation into tendencies emphasizing permanency in both art, and to a lesser extent, daily living. By exploring ideas surrounding temporality, the ephemeral, and permanency in the studio and the gallery by working creatively with organic material and allowing it to decay naturally, I foreground ideas surrounding “life and death,” and the way they play out in art practices.\nThe thesis has been separated into three main chapters, the first one being an Extended Artist Statement where I elaborate on the research interests, artistic influences and material dedication that informed my project. Practice documentation is the focus of the next chapter, where I have compiled images of my work at various stages from the studio to the gallery. I also explain in some detail my decisions concerning experimentation and processes, and provide formal descriptions, titles, and dates. The final main chapter is an interview with Toronto-based artist Laurie Kang, where her practice, process, material choices, and her work that evolves within the gallery are the focus. These three components work alongside with my studio practice and MFA exhibition to question our desire for permanence, and how that desire influences our interactions with making and engaging with art.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".