Bibliographic record
Abstract
“We began to dig ourselves   deeper than we dreamed when we began to see   metal as other than medicine, our bodies, more than mineral.” From an emerging environmental voice comes an evocative, multilayered poetry collection about extraction, destruction, and the erasure of Indigenous people.   At Rabbit Lake in Northern Saskatchewan lies the second largest uranium mine in the western world. For decades, uranium ore and its poisonous by-product—pitchblende, a highly radioactive rock—were removed, transported, and scattered across the land, forever altering the lives of plants, animals, and people who live there.   Elise Marcella Godfrey’s Pitchblende is a powerful, political collection that challenges us to urgently rethink our responsibilities to the land, water, and air that sustains all species, and our responsibilities to one another. Inspired by and adapted from testimonies given at the public hearings about the Rabbit Lake mine, which prioritized the voices of industrial interests, Godfrey gathers voices from the found texts, and adds others, in defence of the natural world. Interconnected, Godfrey's poems are a choral and visual, literal representation of how industry, capitalism, and colonialism seek to erase affected peoples and their voices.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.500 | 0.184 |
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".