Bibliographic record
Abstract
You cannot walk it out. You cannot push it through your body, try as you might. Sturdy steps, winter-boot-clad, through fast falling snow in early morning light—all ice fog and fantastical—are futile. Your heart sits squarely, not moving. Bird in a cage, this heart. Wants only for someone to open the door, to let it out and give it wings. Instead, you try to walk it out. But you cannot push it through your body, try as you might. It aches, beats—persistent—strings tugging on memory like a balloon fastened to a child’s wrist with a loose bow. It is enough to know it beats, without thinking. It is enough. You cannot walk it out, this love. You cannot push it out through the soles of your feet, urging it down into the earth and—underneath that, even—more deeply, into hidden labyrinths of nickel and copper mines. You cannot walk it out. It will not let you. Instead, it begs you to carry it, heavy and laden, tired and weary, from this point of land under winter pines to that one, where the bay curves in like the place where a waist is sculpted by a man’s hand. You cannot walk it out because you know it is enough. And it is also too much.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.828 | 0.719 |
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".