Action learning: towards a framework in inter-organisational settings
Bibliographic record
Abstract
While much of the literature on action learning focuses on managers developing their capacity to learn and transform their own organizations, this article explores how action learning has been used in inter-organisational settings. Two settings are presented: the first an EU-funded management development programme called the National Action Learning Programme (NALP) which ran in Ireland from 1998 to 2000 and the second an EU funded programme, called CO-IMPROVE which commenced in March 2001 and involves inter-organisational networks in three European countries. The essential structure of the NALP approach—the action learning approach and the inter-organisational learning network—has been adopted in CO-IMPROVE. The need here for a well-developed capacity to learn, not only at the levels of individuals or companies, but also at the inter-organisational (or extended manufacturing enterprise (EME)) level required the application of an action learning approach. The application of NALP in such a new and wider organisational setting has promised two potentially desirable outcomes: the rapid facilitation of the particular needs of the CO-IMPROVE research project and the further development of the approach itself. The article describes the two programmes and reflects on (a) the action learning processes in inter-organisational settings, and (b) the outcomes with respect to management and organisational learning that point to ways in which the exciting field of inter-organisational action learning may be developed.
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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 | 0.027 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.080 |
| Scholarly communication | 0.027 | 0.030 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.010 | 0.010 |
| 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".