The Vibrancy of Materiality and Otherwise-Than-Place in Susan Gillis’s Obelisk
Bibliographic record
Abstract
This article deals with Obelisk (2017), a poetry collection by Canadian Susan Gillis (b. 1959) concerned with the impact of human action on Earth in a myriad of forms. Drawing on a wide spectrum of poets, thinkers and artists, including Du Fu, Czeslaw Milosz, Walter Benjamin, John Dixon Hunt, Don McKay, Xi Chuan and Edward Burtynsky, Obelisk looks like an essay in fragments where Gillis assembles the precious insights of her ancestors to shed light on homo sapiens’ intromission into physical space to make the Earth suit human needs. When put together, her heavily annotated and erudite poems read like a denunciation of the indelible mark humans are leaving on the face of the Earth to make it a habitable space, whilst destroying it in the process. However, there is room in Obelisk for a probing reflection on wilderness and place, for a celebration of the vitality of matter and the more-than-human world, for an environmentally-informed critique of the way human action is having a colossal impact on the planet in the age of the Anthropocene, and for a meditation on what poetry can do in the light of environmental degradation to encourage humanity to act and live responsibly on Earth. Thus, Obelisk warns readers against the destruction of the biosphere and celebrates the persistente of poetry as a mode of knowing and as a tool for fashioning an environmental ethics
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".