Bibliographic record
Abstract
Citation (2015), "List of Contributors", Exploring Pedagogies for Diverse Learners Online (Advances in Research on Teaching, Vol. 25), Emerald Group Publishing Limited, Bingley, pp. ix-x. https://doi.org/10.1108/S1479-368720150000025032 Publisher: Emerald Group Publishing Limited Copyright © 2015 Emerald Group Publishing Limited Leanna Archambault Arizona State University, Tempe, AZ, USA Jered Borup George Mason University, Fairfax County, VA, USA Shawn Michael Bullock Simon Fraser University, Burnaby, BC, Canada Richard Allen Carter Jr. University of Kansas, Lawrence, KS, USA Ramona Maile Cutri Brigham Young University, Provo, UT, USA Amy Garrett Dikkers University of North Carolina Wilmington, Wilmington, NC, USA Helen Freidus Bank Street College of Education, New York, NY, USA Dawn Garbett University of Auckland, Auckland, New Zealand Heather Greenhalgh-Spencer Texas Tech University, Lubbock, TX, USA Somer Lewis University of North Carolina Wilmington, Wilmington, NC, USA Alan Ovens University of Auckland, Auckland, New Zealand Brian Joe Rice University of Kansas, Lawrence, KS, USA Mary Frances Rice University of Kansas, Lawrence, KS, USA Mark Stevens George Mason University, Fairfax County, VA, USA Jennifer Thomas Willowcreek Middle School, American Fork, UT, USA Karen Vignare University of Maryland University College, Adelphi, MD, USA Aimee L. Whiteside University of Tampa, Tampa, FL, USA Erin Feinauer Whiting Brigham Young University, Provo, UT, USA Book Chapters Exploring Pedagogies for Diverse Learners Online Advances in Research on Teaching Exploring Pedagogies for Diverse Learners Online Copyright Page List of Contributors University of Kansas Editorial Team Acknowledgments Editor’s Notes Foreword Section I: Promises of Digital Technology for Teaching and Learning Section Introduction: Promises of Digital Technology for Teaching and Learning Digital Technologies and Diverse Learning in Teacher Education: Reassembling the Social Perspective Resource Students’ Use of Internet Inquiry Strategies in an Online Inquiry Project Blended Learning for Students with Disabilities: The North Carolina Virtual Public School’s Co-Teaching Model Section II: Reimagining Support for Online Learners Section Introduction: Reimagining Support for Online Learners Parental Engagement in Online Learning Environments: A Review of the Literature Rhetorical Constructions of Parents by Online Learning Companies: A Study of Parent Testimonials Providing Chances for Students to Recover Credit: Is Online Learning a Solution? Section III: Thinking about Online Practice Section Introduction: Thinking about Online Practice Ecosophic Teaching Using a Pedagogy of the Glocal Mapping Relational Models for Online Teacher Preparation and Professional Development With New Eyes: Online Teachers’ Sacred Stories of Students with Disabilities Afterword About the Contributors
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.728 | 0.731 |
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".