Bibliographic record
Abstract
In autumn, as the schoo l bus pull ed away from our yard, Heath er and I would r un to the hou se, throw aside our schoo l outfits for our worn out “work clothes” and head to th e back forty of our farm, wh er e there was an old wooden granary. We spent hours cleaning that gr anary, though no amount of scrubbing rid it of th e mu sk of decaying wood, barl ey chaff, mou se shit and the debris of swallow nests. If asked wh at we were up to, we’d mutter some thing about playing “pioneer,” th ough this was more of an expedien t explanation than an actual indicator of what we were doing. Together, but isolated — both of us immer sed in our own imag ined worlds — Heather and I staged and restaged the granar y, with rain—bloate d furniture, torn books and knick—knacks th at we found scattere d in th e rock pile behind th e dug— out or in the attic of th e barn . If we got hungry, we’d raid th e ripe garden for food, dodging made—up enemies — wild animals and equally wild sto rybook “Ind ians.” In autumn, th e mer e scent of what’s almost ineffable — dusk air tin ged with stubble fire, cut clover — places me back in that granary. I’m reading Anne l’ Green Gables, while Heath er has qui et conversations with her favourite doll, who m she aptly calls, “Dolly.” I still tease Heather about how she and Doll y bear a funn y resemblance — th ey share reddish, matted curls, limbs scuffed with mud and grass, a few freckles and chee ky grins.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.794 | 0.556 |
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".