Lessons Learned: On Educational Picture Books
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AS ANY TEACHER will corroborate, subjects covered in an elementary school classroom are never limited to those mandated by provincial curricula. Teaching is serendipitous: a geometry lesson can encourage a discussion about architecture, fossils make children aware of their own skeletons and reading William Carlos Williams ’ “The Red Wheelbarrow ” can lead to talk of free-range chickens and or-ganic farming. Thus a teacher must be prepared to answer an infinite number of questions and be willing to defer to outside sources when she does not readily know the answers. As well, a good teacher will become aware of the current concerns of her students and incorporate those concerns into curricular and non-curricular les-sons. At home, in the schoolyard, online or watching TV, children are constantly ex-posed to new ideas and concepts. To answer the questions raised by these new ideas, teachers may draw from their own knowledge first and the Internet second. Additionally, teachers often turn to picture books not only to provide answers but also to generate thorough discussions. Picture books are excellent educational tools precisely because they do more than just simply answer questions. More than most media, good picture books expose children to other worlds and other ways of think-NEWFOUNDLAND AND LABRADOR STUDIES, 25, 2 (2010)
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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 it