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)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.102 | 0.034 |
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".