ESL readers and writers in higher education : understanding challenges, providing support
Bibliographic record
Abstract
CONTENTS Preface Acknowledgements Part I: Understanding Challenges Chapter 1: Understanding Challenges, Providing Support-ESL Readers and Writers in Higher Education Norman W. Evans, Brigham Young University & Maureen Snow Andrade, Utah Valley University Chapter 2: Perceptions and Realities of ESL Students in Higher Education: An Overview of Institutional Practices Maureen Snow Andrade, Utah Valley University Norman W. Evans, Brigham Young University K. James Hartshorn, Brigham Young University Chapter 3: Focusing on the Challenges: Institutional Language Planning William G. Eggington, Brigham Young University Chapter 4: Writing Centers: Finding a Center for ESL Writers Lucie Moussu, University of Alberta, Edmonton & Nicholas David, Divine Word College Chapter 5: Writing Instruction for Matriculated International Students: A Lived Case Study Tony Silva, Purdue University Chapter 6: Familiar Strangers: International Students in the U.S. Composition Course Elena Lawrick, Reading Area Community College & Fatima Esseili, University of Dayton Chapter 7: Academic Reading Expectations and Challenges Neil J Anderson, Brigham Young University - Hawaii Part II: Providing Support Chapter 8: Developing Self-Regulated Learners: Helping Students Meet Challenges Maureen Snow Andrade, Utah Valley University, Norman W. Evans, Brigham Young University Chapter 9: The Research-Instruction Cycle in Second Language Reading William Grabe, Northern Arizona University & Xiangying Jiang, West Virginia University Chapter 10: Supporting Multilingual Writers through the Challenges of Academic Literacy: Principles of English for Academic Purposes and Composition Instruction Dana Ferris, University of California Davis Chapter 11: Assisting ESP Students in Reading and Writing Disciplinary Genres Fredricka L. Stoller, Northern Arizona University & Marin S. Robinson, Northern Arizona University Chapter 12: Corpus-Based Vocabulary Support for University Reading and Writing Mark Davies, Brigham Young University & Dee Gardner, Brigham Young University Chapter 13: When Everything's Right, but It's Still Wrong: Cultural Influences on Written Discourse William G. Eggington, Brigham Young University Chapter 14: Using Technology to Teach ESL Readers & Writers Greg Kessler, Ohio University Chapter 15: Integrated Reading and Writing Assessment: History, Processes, and Challenges Mark Wolfersberger, Brigham Young University - Hawaii & Christine Coombe, Dubai Men's College Contributors
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 teacher head, 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".