Reimagining Education for the Second Quarter of the 21st Century and Beyond
Bibliographic record
Abstract
The authors in this volume offer a new set of lenses that brings into focus the possibilities offered by different pedagogical approaches. With these lenses, this volume recognizes and answers the growing call from learners, parents, educators, communities, and national leaders for a re-imagined way to educate. This volume creates a vision of the future of education that calls for engagement in such pedagogies as blended learning, disruptive technology, connected and personalized. Contributors are: Vinita Abichandani, Fatma Nur Aktaş, Anastasios Athanasiadis, Anastasios (Tasos) Barkatsas, Seth Brown, Athina Chalkiadaki, Grant Cooper, Carlos García Cuadrado, Kimberley Daly, Yüksel Dede, Zara Ersozlu, Andrew Gilbert, James Goring, Anne K. Horak, Kathy Jordan, Katerina Kasimatis, Gillian Kidman, Peter Kelly, Manolis Koutouzis, Alex Koutsouris, Huk-Yuen Law, Susan Ledger, Kathy Littlewood, Simone Macdonald, Elisa Arranz Martín, Tricia McLaughlin, Juanjo Mena, Claudia Orellana, Anastasia Papadopoulou, Vassiliki Papadopoulou, Kate Park, Scott K. Phillips, Ioanna Skaltsa, Micah Swartz, Hazel Tan, and Lisa Williams.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".