Action learning worldwide : experiences of leadership and organizational development
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
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.056 | 0.009 |
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".