Bibliographic record
Abstract
Through my art, I present a critical view of nonhuman animals as emotional beings that mourn and also grieve. The inspiration for my thesis came from an across Canada drive that I took from Kelowna, British Columbia to Windsor, Ontario during which time I saw 140 animals dead on the highway. The installation entitled (un)spoken uses motion sensors, video, photography, and mapping to address mourning, grief, “entangled empathy”, and “rewilding” through the symbolic representation of my journey and the crows that followed me on my way. I see my installation as an entry point for individuals to start to rethink their relationships with nonhuman animals in their daily lives. I base the foundation of my creative work around the theoretical concepts from the ideas of Barbara King, Angus Taylor and others who examine nonhuman animals mourning and grief. The idea of “entangled empathy”, which is at the heart of my work, is taken from the research of ecofeminist philosopher, Lori Gruen. Biologist Marc Bekoff’s ideas around compassion and reconnecting with nature through a process that he refers to as “rewilding” is what I seek to explore with the imagery I have chosen to work with. The aim of the work is for the audiences to recognize possible ways of reconnecting to nature and to encourage them to grow more conscious of the fact that human activity has a significant impact on the animal world.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.368 | 0.144 |
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".