PUSH and GROW Theories in Change Management: Gateways to Understanding Organizational Learning
Bibliographic record
Abstract
This paper comes in two parts. Part One examines an apparent conflict between two approaches to organizational transformation (OT) and the theories they reflect. It examines the differences between these "Push " and "Grow " theories and the possibilities for a new synthesis. While there is plenty of practical significance to this dispute in terms of alternative approaches to change management, the controversy provides a strategic way for students of organizational learning and change to gain insight into these processes. This is a version of the action science argument: to understand a system, try to change it. Part Two builds on the prior discussion to outline an organization theory, using causal loop diagrams, that highlights the importance of the learning loops of organizational learning. Thus we use the OT Push/Grow polarity as a gateway to further insight into organizational learning and to a little progress in organization theory.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".