Using Logic Models and the Action Model/Change Model Schema in Planning the Learning Community Program: A Comparative Case Study
Bibliographic record
Abstract
Abstract: Recent interest has been noted in the evaluation community in expanding the focus from program implementation and outcomes to program design and planning. One important step for moving in this direction is to examine existing evaluation models and to assess their relative strengths and weaknesses for planning purposes. This article presents a comparative case study of applying logic models and the action model/change model schema for planning the Learning Community Program in Taiwan. Lessons learned from these applications indicate that logic models are relatively easy to learn and effective for identifying major program components and indicators, but not sufficient for articulating the theoretical significance of the program. On the other hand, the action model/change model schema requires more time to learn and practise, but it has relative advantages for providing theoretical insights into contextual factors and causal mechanisms of the program, unlike logic models. This comparison can serve as a guide for evaluation practitioners when selecting evaluation tools to apply in planning and/or evaluating their programs.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →2 of 3 models called this metaresearch. This work is contested: it sits on the field's empirical boundary, and whether it counts depends on which model you asked. It is one of the 51 works in the disagreement dossier.
Comparative case study assessing the relative strengths of logic models versus the action model/change model schema as planning and evaluation tools; the object is evaluation methodology, which sits on the boundary between applied evaluation practice and research methods.
This comparative case study evaluates program-evaluation models and their properties as methodological tools.
Compares logic models for planning a learning-community program; program evaluation methods, not evaluation of research.
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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".