Bibliographic record
Abstract
Citation (2012), "List of Contributors", Miller, F.S. (Ed.) Transforming Learning Environments: Strategies to Shape the Next Generation (Advances in Educational Administration, Vol. 16), Emerald Group Publishing Limited, Leeds, pp. vii-viii. https://doi.org/10.1108/S1479-3660(2012)0000016002 Publisher: Emerald Group Publishing Limited Copyright © 2012, Emerald Group Publishing Limited Book Chapters Transforming Learning Environments: Strategies to Shape the Next Generation Advances in Educational Administration Advances in Educational Administration Copyright Page List of Contributors Acknowledgment Introduction Developing Teacher Leaders to transform Classrooms, Schools, and Communities Leading for Change: Designing a Model of Transformational Leadership Through the Carnegie Project on the Education Doctorate Teaching about Religious and Spiritual Difference in a Global Society Incorporating Concepts of Global Citizenry into Student-Centered Academic Advising Collaborative Partnerships for Capacity Building through Professional Development Learning Starts with Design: Using Universal Design for Learning (UDL) in Higher Education Course Redesign The Value of Value-Added Methods Responsive Technologies for Young Adolescents “This I Believe”: Addressing Cultural Competency with the Digital Narratives of Middle Grades English Language Learners From Pong to PS3: How Video Games Enhance Our Capacity to Learn and Build Community Game Changers for Transforming Learning Environments
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.708 | 0.697 |
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".