Evaluation Research: A Pragmatic, Program-Focused, Research Strategy for Decision-Makers
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
Human resource development (HRD) practitioners frequently need to gather and organize data to support decisions about programs. Unfortunately, in many work environments there is a short time available to gather data in support of the decision-making process. Yet the ability to develop or use data or to convince others to use data has become the prime concern of decisionmakers. The evaluation research strategy contains four primary features—utility, feasibility, proprietorship, and accuracy. With a philosophical foundation grounded in pragmatism, evaluation research follows a four-level decision-making hierarchy: purpose, techniques, plan, and implementation. In addition, there are nine major purposes. There are two primary participants in evaluation research: the researcher and the stakeholder group. The stakeholder group is included because of the belief that people who have a stake in an evaluation research outcome should be actively and meaningfully involved in shaping that research effort, thus increasing the likelihood of utilization. Evaluation research may be goal-driven; or it may focus on evaluation questions, concerns and issues, program rationales, decisions or problems, or organization (client) needs.
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 | 0.090 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it