WAC twenty years from twenty years ago: survey evaluating the presence of WAC/WID programs throughout the US and Canada
Bibliographic record
Abstract
Survey Evaluating the Presence of WAC/WID Programs throughout the US and Canada by Tara Suzanne Porter Statement ofProblem 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 ofData Surveys 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., Committee Chair '"l11/ 0 2:.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".