Bibliographic record
Abstract
Citation (2015), "List of Contributors", Accessible Instructional Design (Advances in Special Education Technology, Vol. 2), Emerald Group Publishing Limited, Bingley, p. vii. https://doi.org/10.1108/S2056-769320150000002011 Publisher: Emerald Group Publishing Limited Copyright © 2015 Emerald Group Publishing Limited Randall Boone University of Nevada – Las Vegas, Las Vegas, NV, USA Dave L. Edyburn University of Wisconsin – Milwaukee, Milwaukee, WI, USA Keith D. Edyburn Maternity Neighborhood, Charlottesville, VA, USA Anne Guptill California State University East Bay, Hayward, CA, USA Evelyn Hickey Calgary Board of Education, Calgary, Alberta, Canada Kyle Higgins University of Nevada – Las Vegas, Las Vegas, NV, USA Cyndi Rowland Utah State University, Logan, UT, USA Jared Smith Utah State University, Logan, UT, USA Jonathan Whiting Utah State University, Logan, UT, USA Book Chapters Accessible Instructional Design Advances in Special Education Technology Accessible Instructional Design Copyright page List of Contributors Editorial Advisory Board Accessible Instructional Design: Designing for Differences What Do You Need to Create and Maintain Web Accessibility? Universal Design for Online Learning Accommodation, Access, Large Scale Assessment: Possibilities for Universal Design Refocusing Instructional Design Design for More Types: Designing Text to Support the Access, Engagement, and Success of Diverse Learners About the Authors Index
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.733 | 0.740 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".