CAUCE Institutional Members' Survey: A Snapshot
Bibliographic record
Abstract
Continuing education in Canadian universities is currently at a type of crossroads. It is being affected by a number of factors, including recent changes in the economy; the different approaches universities are taking to continuing education, which range from centralized to decentralized models; and the blending of continuing education with areas such as online and distance education. Given these circumstances, the CAUCE Executive, under the leadership of Tracey Taylor-O'Reilly, CAUCE president, and Lorraine Carter, its Research Committee chair, designed and disseminated an institutional members' survey in Spring 2009. The ultimate goal of this initiative was to generate a snapshot of the needs of CAUCE's institutional members and to use these findings to plan programs and services that reflect the needs of the membership. Further, the Executive intends to repeat the survey every few years. This article reports the key findings of the survey as descriptive statistics and recurring messages offered in open ended questions and as additional comments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".