Big Shoes to Fill: An Evaluation Journey in the Footsteps of Daniel L. Stufflebeam
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.084 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it