Bibliographic record
Abstract
Abstract Since evaluation does not currently accredit preparation programs, the content of this chapter was virtually a blank slate for the authors. Fittingly, they began by reviewing some background literature as to basic terms, what accreditation might entail, and critically parsing out the benefits of doing so. From there they proceeded to examine fields with a kinship to evaluation (accounting, business management, and human resource development) that have implemented systems to provide accredited status for their educational programs. The results have been questionable and led to perhaps unanticipated recommendations for what evaluation should be considering. © Wiley Periodicals, Inc., and the American Evaluation Association.
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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 | 0.082 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.029 | 0.061 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".