Goal Attainment Scaling in Action Research: Enhancing a Systems Thinking Orientation
Bibliographic record
Abstract
Awareness of conducting action research (AR) with a systems thinking orientation is increasing. We consider such orientation to be a fundamental, grounding, feature of AR. However, little exploration has occurred of how evaluation activity within AR, using a tool such as Goal Attainment Scaling (GAS), might enhance this orientation. We offer an argument for utilizing GAS to not only broaden action and deepen the research in individual AR projects, but also, for learning more about AR at a meta‐level where there is the increased complexity when evaluating multiple AR projects in diverse contexts. As well as theorizing the concepts noted, an outline of the way the evaluative study of action research (ESAR) customized and deployed GAS for evaluation of AR at both individual and meta‐levels is provided. The paper is a unique contribution to the domains of AR, systems thinking and evaluation methods. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.140 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".