Bibliographic record
Abstract
Someone posted a photo of a red squirrel, pine cones in soft focus all around so it floats in the middle of the screen though it's perched on a branch.Someone else thought a white-tailed fawn was catching a bit of sun when it was probably doing just that.Three swans warp tissue-like above a green lake I've never seen, a place I'd like to take you, but I leave you to sleep, think of kissing you awake, don't do it because someone added a photo of a blackbird and titled it "John."That's all.This is bothering me because I am not sure if the bird would look regal without being named.I am corrupt.Elsewhere snow has piled on dried twigs in cones like cotton and light cuts the sky in shards so I think of God on my Norton Anthology of Romantic Literature.The water is so pink in Tadoussac that the uploader had to say it wasn't edited.But we're getting too far away from what I've come to say.I dreamed of ten-feet waves in slo-mo, all foam and no water, airing out against a shore, muddy and brackish like holey sheets cut off a tired moon.Ghost waves.Then a friend sent me a photo from work of the foamed beach in your hometown and I wonder what it means to take root, how plants endure shock being repotted.But I am not talking about displacement, I am taking about home.Can a guest ask a question that is a gift?I want to ask and ask and ask because I know we are at our best when we are leaving happy and giving, like the people who take photos and upload them, and the people who look at those photos not knowing why, possibly beauty, possibly hoping to step outside their worlds for a few minutes each morning like prayer, surrendering to life without our intervention -beside ourselves.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.075 | 0.010 |
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".