Bibliographic record
Abstract
In a lifetime of composing excuses and their alternatives, I have algebraized many such excuses for my writing. Rage, frustration, the trepidation of answering the ancient litany of the repetitive male voice declaring itself agent, keeper, and writer of all valid and valued experience. Fear of failure, the containment of patriclinous inheritance, infects my joy, my pleasure in language. Fear and joy wrestle to control the addictive and crazy tenacity of my yearning to language Joan of Arc's burning and statutory rape, to language endive and gouda cheese and the bakery in Camrose that sold brownies, to language the tough-rooted buffalo beans that bloomed in the ditches of my childhood. Tenacity, for its own sake, clinging to words, and the joy I fear that keeps words rooted, like those tough-stemmed wildflowers that signalled the arousal of spring in my Canadian prairie. We could not pick them - they refused to succumb to jam jars or vases; but we could pluck a labial blossom and suck, from its thin stamen, a tinge of incipient honey. Waiting for the rotund school bus that would carry us into town, we stood at the end of the lane and suckled wild sugar, that invitation to the bees, from buffalo beans. And for a moment, our sadness would evaporate.
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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.218 | 0.227 |
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".