Assessing the efficacy of group model building workshops in an applied setting through purposive text analysis
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
Abstract Group model building (GMB) approaches have been shown to improve participants' understanding of complexity by shifting and aligning individuals' mental models of the interconnections within complex systems. However, reviews of GMB literature have identified knowledge gaps for assessing the efficacy of GMB activities. To address these gaps, these studies recommend assessing multiple cases, shifting from controlled to applied settings, and reporting on objective measures. We address each of these items by comparing the outputs of multiple community‐based GMB workshops to participants' mental models elicited through pre‐workshop interviews. Using purposive text analysis, we developed causal loop diagrams for comparison to a group workshop model. Through a quantitative analysis, we find that individuals convened in GMB workshops have greater alignment on factors, causal links, and feedback. We believe these contributions can help other GMB practitioners better assess the efficacy of their activities with more rigor and detail. © 2020 System Dynamics Society
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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