The Pirate's Bed by N. Winstanley
Bibliographic record
Abstract
Winstanley, Nicola. The Pirate's Bed. Illus. Matt James. Toronto: Tundra Books, 2015. Print.Morning Class"I see a smiling mouth [on the bed]! It's smiling!""Why is his toe red? He has boo boos on his feet.""I liked when the pirate found an island. I thought there was going to be a treasure!""I liked when the pirate got a new bed.""I liked when the boy had pirate dreams!""I'm glad the pirate wasn't eaten by a hammer shark.""I loved the palm trees.""I like when the pirate got a new bed.""How is that bed inside the water? Why it got eyes?""I liked everything about this book!""I liked the hammerhead shark."Afternoon Class"The boy has pirate dreams!""The girl dreams about dolphins, jellyfish, crabs and starfish!""I liked when the boat crashed in to the rocks...boom!""I like when the bird visited.""I like those hammerhead sharks!""I had a dream about a bad guys eye that looked like that!""I like when the bed's eye peeked out of the portal. How did it do that?"["The illustrator drew him like that!"]"I liked that pirate ship.""Why was there a monster [waves] grabbing the ship?""I am very, very scared of this book!""I like when the bed went back and forth, back and forth in the storm."Reviewers: Students from the Child Study Centre’s Junior Kindergarten Program in the Faculty of Education at the University of Alberta
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.317 | 0.230 |
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".