Bibliographic record
Abstract
Some poems can live without souls / but mine remain ghastly fools flicking / uncomfortable narratives like / cigarette butts during class change. One out of every twenty students in the adult education classes Evan J teaches in Sioux Lookout, Ontario, dies every year; the surviving students are often afflicted by severe racism, poverty, addictions, and violence. Ripping down half the trees engages with these struggles, offering a catalogue of experiences specific to the remote regions of Canada. Tearing down the façade of Canadian justice and equality to expose the racism, colonialism, sexism, prejudicial capitalism, and ableism at the nation's core, these are poems about cruelty, both the obvious and the ambient. They are unflinching in their sociopolitical criticism, upset by unchanging systemic oppressions, unable to overlook the threat of the author's white skin, unwilling to forget Justin Trudeau in blackface. And while they acknowledge the limits of the author's privileged perspective, they are never willing to let the perpetrating structures of this cruelty go unchecked. But these poems also let stand the shelterwood, the upstanding actions of individuals, the totems of hope. They work as coping strategies, as therapy, as empathy, offering a glimpse of optimism and a space for discourse. These are poems that listen.
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 | 0.016 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".