“Mossification”: Subverting the Human-Centric Portrait with a More-Than-Human Triptych
Bibliographic record
Abstract
Abstract Motivated by a sense of ethical obligation and environmental urgency, I commissioned a Toronto-based artist to take a portrait of a “more-than-human” me that embodied nonhuman elements. The aim of this artistic endeavor was to re-evaluate humans' impact on and relationship with the organic and non-organic beings and stuff with which we are entangled. My aim with the resulting portrait, “Mossification,” was three-fold: to visually represent a more-than-human (multiple-singular) self; to subvert the human-centric portrait by giving moss and lichen more visual space and symbolic agency; finally, to suggest, through movement in the form of a triptych, that if we do not change, humans will end up buried under by nature. This short essay is broken down in three parts. In the first two, I provide philosophical context then synthesize a brief history of portraiture with the aim of showing how “Mossification” subverts the genre. In the final part, I demonstrate how “Mossification” might be positively received but nonetheless fails to embody transcorporeality because of its entanglement with neoliberal systems that instrumentalize and objectify nature. I conclude that even though “Mossification” is problematic, it remains a productive visual experiment because of its generative capacity to destabilize human-centric representative traditions and symbolic codes.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".