You May Say I’m a Dreamer: Promoting Positive Organizational Change
Bibliographic record
Abstract
The rapidity and intensity of changes spurred by demographics, technology, COVID-19, supply chain practices, and war, among many others, require organizations around the globe to adapt and survive. Importantly, however, change does not have to be limited to fixing the wrongs and passively adjusting to the inevitable. In this symposium, we explore how change can be a positive force for a better future, and how organizations can leverage change management practices to bring out the best of the new normal using a positive organizational lens. The Positive Organizational Scholarship (POS) movement that started in the early 2000s seeks to understand organizational strengths rather than weaknesses as it focuses on positive characteristics, processes, and outcomes (Cameron et al., 2003). Rather than focusing solely on resolving problems, POS looks at change through an optimistic lens and sees it as a positive development that can advance organizations further, helping reach peak performance through strengths rather than just correcting failures. One such example of using a positive approach to change is the appreciative inquiry (AI) method. This unique method was conceived to help generate new ideas on optimal organizing (Cooperrider & Srivastva, 1987; Cooperrider & Whitney, 2005), allowing organizational stakeholders to identify their path forward by taking a strengths-based approach and highlighting capabilities to manage change. Consistent with this perspective, our symposium aims to understand the role of positive change in organizations. This collection of papers explores change processes on different levels of analysis and in different contexts, examining how change can contribute to well-being of organizations and their members, helping them to not only overcome challenges but create a better, brighter future. Making Impact Personal: The Mind and Heart of Business Leaders Doing Good in the World Author: Jane Day; PhD student at Weatherhead School of Management, Case Western Reserve U. Author: Ronald Fry; Case Western Reserve U. Author: David L Cooperrider; Case Western Reserve U. Author: Dave Ulrich; U. of Michigan Let’s Get Aboard: Positive Collective Engagement as a Dynamic Capability in Transformations Author: Mette Storvestre; NHH Norwegian School of Economics Appreciative Inquiry Summit in a Suburban Church Author: Eric Sanders; Elmhurst U. Author: Traci Smith; Elmhurst Presbyterian Church Formalized Curiosity: Researching the AI Research Method Author: Mirit K. Grabarski; Lakehead U. Author: Megha Yadav; U. of Wisconsin, Milwaukee Author: Maria Mouratidou; U. of Cumbria, UK Author: Hina Kalyal; U. of Western Ontario Stories of Positive Change: Teachers' Journeys towards Fulfilling their Callings Author: Hayuta Yinon; Oranim College of Education
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".