“What Do You Mean I Wrote a C Paper?” Writing, Revision, and Self-Regulation
Bibliographic record
Abstract
Students often express surprise at their grades on papers. This gap between expectation andachievement may stem in part from lack of facility with revision strategies. How, then, can teacherswork with their students to foster more effective revisions? This question in teaching and learninghas inspired an interdisciplinary collaboration: one of us is a management and leadership professor(Sharen), and the other is an English/communication professor (Feltham). In this essay, we describea research study from winter 2013 in which we explored how a series of interventions improvedstudents’ mindsets about the process of drafting and revising reports for a second-year-universitycourse entitled “Women and Leadership.” After outlining key aspects of this study that we feel are ofgeneral interest, we then present a series of reflective suggestions about how to teach revision derivedfrom both our experiences and a selective survey of the literature on both revision and self-regulation.
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 | 0.028 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), 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".