Bibliographic record
Abstract
For undergraduates following any course of study, it is essential to develop the ability to write effectively. Yet the processes by which students become more capable and ready to meet the challenges of writing for employers, the wider public, and their own purposes remain largely invisible. Developing Writers in Higher Education shows how learning to write for various purposes in multiple disciplines leads college students to new levels of competence. This volume draws on an in-depth study of the writing and experiences of 169 University of Michigan undergraduates, using statistical analysis of 322 surveys, qualitative analysis of 131 interviews, use of corpus linguistics on 94 electronic portfolios and 2,406 pieces of student writing, and case studies of individual students to trace the multiple paths taken by student writers. Topics include student writers’ interaction with feedback; perceptions of genre; the role of disciplinary writing; generality and certainty in student writing; students’ concepts of voice and style; students’ understanding of multimodal and digital writing; high school’s influence on college writers; and writing development after college. The digital edition offers samples of student writing, electronic portfolios produced by student writers, transcripts of interviews with students, and explanations of some of the analysis conducted by the contributors. This is an important book for researchers and graduate students in multiple fields. Those in writing studies get an overview of other longitudinal studies as well as key questions currently circulating. For linguists, it demonstrates how corpus linguistics can inform writing studies. Scholars in higher education will gain a new perspective on college student development. The book also adds to current understandings of sociocultural theories of literacy and offers prospective teachers insights into how students learn to write. Finally, for high school teachers, this volume will answer questions about college writing.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.982 | 1.000 |
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; both teacher heads 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".