Bibliographic record
Abstract
Promote your students’ creativity and get them excited about learning! In the second edition of this popular, practical book, authors Denise Krebs and Gallit Zvi show you how to implement Genius Hour, a time when students can develop their own inquiry-based projects around their passions and take ownership of their work. Brought to you by MiddleWeb and Routledge Eye On Education, the book takes you step-by-step through planning and teaching Genius Hour. You’ll learn how to guide your students as they: ● inspire learning and brainstorm wonders; ● develop inquiry questions based on their interests; ● conduct research and experiments about their topic of choice; ● create presentations to teach their fellow students in creative ways; and ● present their finished product for a final assessment. This edition includes new chapters on managing your classroom projects and recommended books. Throughout the book you will find voices from the Genius Hour community sharing real-life stories and inspiration. Appendices contain handy FAQs and ready-made lessons and resources. In addition, a companion website, www.geniushourguide.org, offers bonus materials and regular updates to support you as you implement Genius Hour in your own classroom.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.312 | 0.215 |
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".