Research to Develop a Consensus Self-Evaluation Model of National Norms of Excellence for Alternating Cooperative Education Programs at Four-Year Colleges and Universities
Bibliographic record
Abstract
The research described in this dissertation was conducted in response to an expressed need for the development of national norms of excellence for cooperative education programs in the United States in 1980. In academic year 1981-1982 a Delphi technique was used with 12 cooperative education experts, who identified 155 cooperative education program norms of excellence specifically for four-year alternating cooperative education programs. In academic year 1982-1983, the 155 norms identified were transposed into a 90-item self-evaluation questionnaire which was field tested and sampled at 14 colleges and universities with alternating cooperative education programs in the United States. Of 900 college administrators, faculty, cooperative education coordinators, students and employers contacted, 730 responded (81%). The alternating cooperative education program consensus self-evaluation model developed was the first of its kind in the United States. With further refinement and testing it could be adapted for use by other cooperative education programs. Appendices include directions for conducting a Delphi Technique, directions for conducting cooperative education program self-evaluation, anecdotal comments from respondents, and definitions of cooperative education provided by respondents.
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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 | 0.207 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".