Interactive Logic Models: Using Design and Technology to Explore the Effects of Dynamic Situations on Program Logic
Bibliographic record
Abstract
Logic models are commonly used by evaluators to illustrate relationships among a program's inputs, activities, outputs, and outcomes. They are useful in helping intended users develop programs, communicate a program's theory of change, and design evaluations. However, as static documents, logic models can be challenging to build, work with, and present to stakeholders, especially for large and complex programs. Challenged by this inadequacy of static logic models, a program evaluator turned to a graphic designer and a software developer for help. Together, this interdisciplinary group developed web-based software (Dylomo) that allows evaluators to create logic models that better communicate the logic within the model. In this paper, we describe the process by which this interdisciplinary group created this new technology—including a user-testing experience at the Canadian Evaluation Society Conference in Canada in June 2016—to build and present logic models that use interactivity and allow program evaluators to more easily demonstrate the logic within a complex program and to visually explore the potential effects of changes within the program's landscape. This software is freely available on the web, so readers can apply it to their own evaluation practice.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".