Bibliographic record
Abstract
My MFA thesis recognizes the interconnectedness of all living beings. The paintings I created immerse the viewer in landscape scenes spanning anywhere between eight feet in height to eighteen feet in width. Inspired by natural formations, an important aspect of my studio practice is engaging with my sensory apparatus (sight, sound, and touch) through which I attempt to materialize how I perceive other life forms and environments. In short, my painting methodology is heavily process-based where intuition and the senses direct the trajectory of the work. The pictorial language of my paintings is largely based on organic and biomorphic imagery which appears to grow, slink, and unfurl through the space of the paintings. These travelling forms on the canvas parallel the sensory processes of the human body. Conceptually I also explore my relationship to nature, the past and future through material means and methods such as rotating the canvas while painting, thinning and thickening the paint, pooling colours, and layering brushstrokes (Fig. 1). Through this, I seek to embed myself in the material processes, forming highly saturated, dense landscapes that speak to the vastness and evolution of nature as well as our own human embeddedness in it. I chose landscape as the main theme of my work because it represents an accumulation of deep time, present in rock formations, gigantic trees and ancient spaces that were formed over millions of years. More importantly, the resilience of nature resides in the fact that its creation is ongoing just like my painting practice which also evolves as each painting informs the next. Through its conceptual and formal elements this thesis exhibition considers nature’s resilience, that is the ability to rise above ecological disasters, such as extinction, wildfires, flood and draught — nature’s ability to survive.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.037 | 0.016 |
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".