Bibliographic record
Abstract
Abstract This essay examines how two very different thinkers address the question of how to live loss. The first is the Canadian Cree artist and writer Tomson Highway, author most recently of Laughing with the Trickster: On Sex, Death, and Accordians, and the second is US environmental writer Elizabeth Rush, author of Rising: Dispatches from the New American Shore. In two very different modalities of knowledge making, we see a shared quest for a planetary subject able to live loss in a clear-eyed and affirmative way. For Highway, the question is how to live loss, beginning with language loss, without losing the capacity for laughter and joyfulness. The arts of the trickster are his answer. For Rush, the question is: what changes can we be making now to head off crisis, and what language can make such change meaningful and desirable? We also see the two writers striving to make language respond to the challenge of scale. How do you capture the gigantic without being abstract? Highway moves to the mythic, arguing for Indigenous pantheism. Rush tacks between the particulars of planet science and the personal narratives of those living the loss of place.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.050 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".