Bibliographic record
Abstract
Again Around the MaypoleIn the summertime, when the sun takes the sky captive till you forget there has ever been a winter, you can see everything from the schoolhouse windows.Th e whole town, nothing more than the last stop of a railroad caught between glacier and copper-fi lled mountain, fi lters through the bends in the glass.If anything could grow out here, they would put us to work for a few months and make us useful, but the ground is frozen three feet down and lessons stretch all year while the miners drain the mountain.It is recess, so I stay in the back room, fl ip the pages of an old biology primer, and watch the boys play shortball on the narrow diamond ploughed into the moraine.Th ere are only eight of them, so it isn't much of a game.I watch the younger girls run around the maypole, ducking and jumping and carefully brushing dried mud off the edges of the ribbons.And I watch Lizzy Jennings sneaking out of the workmen's bunkhouse, creeping along beneath the railroad trestle that runs through town, trying to make it back to the door of the schoolhouse unseen.Back home, we never had school during the summer months.Mother would take me (and William, once he was old enough) into town on the streetcar, and we' d explore the city shops for hours before heading back to Mission Hill.But home hasn't been home for three years now.Summer outings here are reduced to the recess hour, and Annie Osburn
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.347 | 0.136 |
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".