Chapter 5. Integrating Writing into the Disciplines: Risks and Rewards of an Alternative Independent Writing Program
Bibliographic record
Abstract
2005-06, when there were only 45 institutions with such a major (CCC Committee on the Major in Writing and Rhetoric, 2009).To McLeod, a "robust research agenda and a thriving writing majors" will offer writing programs the best chance to achieve independence (2006, p. 532). THE CENTRAL ISSUES: WHERE ARE WE NOW?In this introduction we have considered the relatively brief history of the evolution of independent writing programs and departments, along with the issues that have been raised (primarily) in the literature on writing program administration.Our first observation is the dominance of the "separation narrative" in this literature, particularly after 1990 when most independent programs and departments began to separate from their home departments.(Of course, we recognize that a number of independent departments existed before this date.However, before this time, generally speaking, they were likely anomalies; following this they may be considered to be part of a disciplinary trend.)A second observation, drawn largely from the work of James Berlin, is that institutional and disciplinary issues that have led to separation have a long and complex history connected to the evolution of the American professional university.As the university continues to evolve there is not a single trend, but many.Liberal arts
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".