Action learning worldwide : experiences of leadership and organizational development
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
Preface PART 1: WHAT IS ACTION LEARNING?: CONTEXT AND APPROACHES Action Learning: The Classic Approach K.Weinstein Action Reflection Learning and Critical Reflection Approaches L.Yorks, J.O'Neil & V.Marsick Business Driven Action Learning: Why and How Organisational Learning and Leadership Development Must be Greater than the Rate of Change Y.Boshyk PART 2: ACTION LEARNING IN NORTH AND SOUTH AMERICA How Some Companies Plan and Design Action Learning Management Development Programs in the United States: Lessons from the Practise S.Hicks General Electric's Action Learning Change Initiatives B.Davids, C.Aspler & B.McIvor Using Action Learning to Develop Human Resource Executives at General Electric P.Tourloukis Getting to the Future First S.Byrd & L.Dorsey Learning as an Adventure in a High-Growth Environment D.Hopkins Action Learning in the Public Sector: The Canadian Civil Service C.Brassard Action Reflection Learning in Latin America I.Rimanoczy PART 3: ACTION LEARNING IN EUROPE, THE MIDDLE EAST AND AFRICA Business Driven Action Learning in the Nordic Region A.Reinholdsson Strategic Executive Learning and Development in French Multinationals N.Rolland Changing the Rules at the World Council of Churches K.Raiser & R.M.Gould Executive Development in Poland G.Lebkowska Action Learning in Israel S.Maital, S.Cizin, G.Gilan & T.Ramon Action Learning in South Africa B.Isaacson PART 4: ACTION LEARNING IN ASIA PACIFIC Competing for the Future: Action Learning and Korean Multinationals T.Lee Business Driven Action Learning in Japan M.N.Honjo Strategic Change Management at Merck Hong Kong R.Pearson Building Internal Capacities for Change: Action Learning in the Public and Private Sectors of China L.Yiu & R.Saner Action Learning Resources and Bibliography Y.Boshyk, M.Rolland & N.Rolland About the contributors Index
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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