Book Review: Teaching history with big ideas
Bibliographic record
Abstract
History is a word about which people will have strong opinions.For those who are intrigued by past events or individuals, history will emanate questions and interest.For others, the word alone will instill fear accompanied by confessions of dislike, a negative classroom experience, or lack of understanding.Various history classes are required in school curricula throughout students' academic careers, whether they like it or not.Regardless of how the majority of the population feels about history, there are two issues often found in classrooms.First, many students will ask why they have to learn about the past since they assume it has nothing to do with them, and secondly, teachers face the challenge of making history relevant and meaningful to students in a standards based classroom.S. G. Grant and Jill Gradwell's new edited book Teaching history with big ideas seeks to explore and address these two issues through the eyes of eight practicing history teachers, who the editors consider ambitious teachers.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.057 | 0.014 |
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".