Bibliographic record
Abstract
Citation (2014), "List of Contributors", Measuring Inclusive Education (International Perspectives on Inclusive Education, Vol. 3), Emerald Group Publishing Limited, Bingley, pp. ix-xi. https://doi.org/10.1108/S1479-363620140000003014 Publisher: Emerald Group Publishing Limited Copyright © 2014 Emerald Group Publishing Limited Joseph S. Agbenyega Monash University, Clayton, Victoria, Australia Donna Barrett Northland School Division No. 61, Peace River, Alberta, Canada Jessica Bucholz University of West Georgia, Carrollton, GA, USA Suzanne Carrington Faculty of Education, Queensland University of Technology, Brisbane, Australia Dianne Chambers University of Notre Dame, Perth, Western Australia Sarah Copfer University of Western Ontario, London, Ontario, Canada Meng Deng University of Beijing, Beijing, China Joanne Deppeler Monash University, Clayton, Victoria, Australia Mary Doveston University of Northampton, Northampton, UK Kymberly Drawdy Georgia Southern University, Statesboro, GA, USA Mary Louise Duffy Florida Atlantic University, Boca Raton, FL, USA Jennie Duke Faculty of Education, Queensland University of Technology, Brisbane, Australia Serge Ebersold National Higher Institute for Training and Research for the Education of Young Disabled Persons and Adapted Teaching (INS HEA), Paris, France Lani Florian University of Edinburgh, Edinburgh, UK; University of Vienna, Wien, Austria Chris Forlin Hong Kong Institute of Education, New Territories, Hong Kong Janet I. Goodman Haralson County Schools, Tallapoosa, GA, USA Michael Hazelkorn College of Coastal Georgia, Brunswick, GA, USA Catherine Howerter Georgia Southern University, Statesboro, GA, USA Johnson Jament University of Northampton, Northampton, UK; Venad Education & Social Services, Kerala, India Anne Jordan University of Toronto, Toronto, Ontario, Canada Agnes Gajewski Centennial College, Toronto, Ontario, Canada Julie Lancaster Charles Sturt University, Bathurst, New South Wales, Australia András Lénárt European Agency for Special Needs and Inclusive Education, Odense, Denmark Donna Lene Indooroopilly State High School, Brisbane, Queensland, Australia Tim Loreman Concordia University College of Alberta, Edmonton, Alberta, Canada Donna McGhie-Richmond University of Victoria, Victoria, British Columbia, Canada Laisiasa Merumeru Pacific Islands Forum Secretariat (PIFS), Suva, Fiji, Pacific Islands Susie Miles Manchester Institute of Education, University of Manchester, Manchester, UK Jayashree Rajanahally Brindavan Education Trust, Bangalore, India Richard Rose University of Northampton, Northampton, UK Umesh Sharma Monash University, Clayton, Victoria, Australia Jacqueline Specht University of Western Ontario, London, Ontario, Canada Jennifer Spratt University of Aberdeen, Aberdeen, UK Amanda Watkins European Agency for Special Needs and Inclusive Education, Odense, Denmark Book Chapters Measuring Inclusive Education International Perspectives on Inclusive Education Measuring Inclusive Education Copyright Page List of Contributors Series Introduction Introduction to Volume 3 Conceptualising and Measuring Inclusive Education Ethical Challenges and Dilemmas in Measuring Inclusive Education What is Effective Inclusion? Interpreting and Evaluating a Western Concept in an Indian Context Data Collection to Inform International Policy Issues on Inclusive Education Resourcing Inclusive Education Measuring Effective Teacher Preparation for Inclusion Leading Inclusive Education: Measuring ‘Effective’ Leadership for Inclusive Education through a Bourdieuian Lens Identifying Effective Teaching Practices in Inclusive Classrooms Measuring Indicators of Inclusive Education: A Systematic Review of the Literature Learning about Inclusion from Developing Countries: Using the Index for Inclusion Using Networking to Measure the Promotion of Inclusive Education in Developing Countries: The Case of the Pacific Region School and Classroom Indicators of Inclusive Education Assessing Teacher Competencies for Inclusive Settings: Comparative Pre-Service Teacher Preparation Programs Developing and Using a Framework for Gauging the Use of Inclusive Pedagogy by New and Experienced Teachers Using Graduation Rates of Students with Disabilities as an Indicator of Successful Inclusive Education 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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.724 | 0.727 |
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".