Bibliographic record
Abstract
I read and write and teach poetry because I hold a long commitment to the efficacy of poetry for transforming our hearts, imaginations, intellects, conversations, and communities. I promote a curriculum of poetry as a curriculum of possibility for learning to live poetically in the world, for learning to live in the ecotone, the fecund place of tensions where conflicts are integral to vitality, education, and transformation. I often hear the question, Is it a good poem? I think we should ask, What is a poem good for? I am eager to bear witness to poetry, to invite a conversation with poets I have lingered with, to spark a little enthusiasm among others, to remind all of us that poets are pursuing their art and living with keen desire. So, in this paper I ruminate on possibilities for responding to the question, “What is a poem good for?” In my ruminations I do not attempt to be definitive; I am only eager to continue a conversation that is ongoing. I present a performative text that is both poetic and full of poetry. I invite colleagues to receive this essay like a long poem, to see with the eyes of the heart, and to hear with ears that are attuned to resonances and silences, and to linger with language and memory and hope.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".