Big Shoes to Fill: An Evaluation Journey in the Footsteps of Daniel L. Stufflebeam
Bibliographic record
Abstract
Background: Evaluation has evolved remarkably since the early 1960s, largely due to the innovative contributions of Daniel Stufflebeam and his colleagues. As a pioneer of evaluation methods, some of the notable achievements arising from Stufflebeam’s work include the context-input-process-product (CIPP) model, evaluation standards, and evaluation checklists. Purpose: The purpose of this paper is to explore Daniel Stufflebeam’s journey beginning with the early days of evaluation through to his retirement and unfortunate passing at 80 years old in 2017. Key features of the CIPP model are considered within a context of other popular models for comparison with the goal of finding relevance for use of CIPP evaluation in education settings. Setting: Not applicable. Intervention: Not applicable. Research Design: Literature review. Data Collection and Analysis: Not applicable. Findings: Stufflebeam’s CIPP model and evaluation standards remain prominent in evaluation practices and his legacy will lay the foundation for future evaluators through continued professional development.
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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 | 0.069 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".