WAC twenty years from twenty years ago: survey evaluating the presence of WAC/WID programs throughout the US and Canada
Bibliographic record
Abstract
In this thesis, I hope to help answer the question of "Where is WAC now?" in my discussion ofthe results ofa survey I conducted with Dr. Christopher Thaiss that reports on the presence and components of WAC programs at US and Canadian institutions. Sources ofDataSurveys with approximately 15 questions were sent to over 2,000 institutions ofhigher education.1,359 individuals from institutions across the US and in Canada responded to this information, making it one ofthe largest surveys on Writing Across the Curriculum. Conclusions Reached Four strong conclusions could be drawnfrom the results ofthe survey:(1) the number of WAC programs has increased over the past twenty years, (2) program leadership relies less on the original director ofthe program and instead switches every couple ofyears, (3) the majority of WAC programs are less than ten years old, and (4) most programs are directed by associate/full professors.,
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".