Evaluation Research: A Pragmatic, Program-Focused, Research Strategy for Decision-Makers
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.
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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 | 0.526 | 0.418 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.034 | 0.026 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".