Comparing the Generativity of Problem Solving and Appreciative Inquiry
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
Appreciative inquiry (AI) theorists claim AI is a more generative form of inquiry than problem solving; this study uses a classical field experiment to test that claim. We test three different processes for producing generative ideas defined as new ideas that motivate new actions. Why AI may be better at producing such ideas is explored and a method for amplifying those qualities (synergenesis) is described. Hypotheses are tested by assessing ideas produced from groups of employees at an urban transit organization. Synergenesis-based groups scored significantly higher than either of the other groups on ratings of generative ideas. Examination of participant’s pre- and post semantic maps show predictable differences in the effects of problem solving and appreciative approaches on engagement of employees in the ideation phase of a change process, consistent with AI claims. Implications for practitioners and suggestions for future research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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