Bibliographic record
Abstract
I was fortunate enough to discover action learning at the foot of the guru himself, Dr Reg Revans -via a seminar held at Salford University in the UK.Dr Revans' impact on my thinking was instantaneous.His words and lectures gave me an insight that in the world of rapid and ongoing organizational upheaval, my major tool for making sense of what was happening was my personal ability to ask questions with others in similar situations.Instead of looking for others to have the answers, I had to find others who also asked similar questions and who were ready to explore new actions, as the familiar ones were not bringing us to the conclusions we sought.Good questions in the company of others led to good insights and different actions.It became my own scientific method for learning and moving forward in the organizational chaos I was experiencing at my workplace.Fast forward a couple of years and I decided to further my interest in Action Learning by using it as the foundation for a doctorate in leadership and organizational development using a new workplace as my personal laboratory.Yes, my encounter with Dr Revans had whetted my appetite for further study into the complexities of management and leadership.One of my colleagues in the international business school where I undertook my doctoral dissertation was a similar student, Richard Hale, who just happened to be working with another of my learning gurus, Alan Mumford.After graduating, Richard and I lost touch -not surprising really given that we were living in different countries (I in Canada and he in the UK) -but when the opportunity arose to put together a special issue of Leadership in Health Services specializing in Action Learning, who better to ask to be the guest editor than Dr Hale himself?Health Care Service is perhaps one of the most complex and rapidly changing fields in our world today.Comprising business interests, technology, politics, specialties and personal calling (amongst others), health care represents the largest area of employment in the entire globe.We have witnessed that ready-made top-down solutions offered by politicians and business gurus make problems different but do not resolve them.The extreme complexity of health systems and the pressures of ever-increasing costs drive governments to distraction.There are no ready answers to the issues that arise, from the management of the system to the leadership that is required.In this morass of complexity, action learning offers a way forward, by having those in the field address the problems they face, instead of waiting for others to come in with pre-ordained solutions that do not work.This issue specializing in Action Learning and health service offers some insight into some of the action learning programs that are operative.What is surprising is that there is not more interest in Action Learning and its ongoing potential for resolving the problems that we are facing in health.As Dr Revans himself said, "We must learn to be ahead of the change curve, not just manage it".We can do this through an ongoing implementation of action learning programs that encourage us to ask good questions and take corresponding actions to move us forward against the pressures of organizational inertia.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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 teacher head, 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".