Bibliographic record
Abstract
A few weeks ago, there was a loud knock on my front door, and there on the doorstep was a large parcel; my long awaited teaching assistant had arrived. Inside the parcel was a copy of Mary Slattery's Teaching with Bear and of course Bear himself, wearing a smart blue jacket and feeling a little worse for wear after his long trip across the Atlantic in the hold of a plane. After settling him in with some honey muffins, we sat down together to review the book and its accompanying DVD. Teaching with Bear is designed for the young learners’ elementary classroom (up to the age of about 11) and consists of a 25-cm bear hand puppet, a teacher's book, and DVD. The teacher's book provides a guide to using and teaching with Bear and the accompany DVD serves to bring the content to life, which is especially vital for teachers who may feel unsure about using a puppet in the classroom. I would recommend watching part of the DVD straightaway after reading the Introduction because the rationale for using Bear becomes immediately evident. Teachers can then go back and work through the chapters after this initial taste. I think the delightful clips filmed in a number of young learner classrooms would inspire all but the most jaded teachers.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.080 | 0.031 |
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".