Using Action Learning with Multicultural Groups
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
Action learning was developed by British physicist and professor Reginald Revans over 50 years ago and has had a growing degree of success in the Western/Anglo-Saxon cultures of the U.S., Canada, northern Europe, Australia, and New Zealand. Few examples of successful implementation of action learning exist, however, with the remaining 90% of the world. Un-awareness of action learning may account for some of the limited use of action learning in these regions. The author contends, however, that another reason may be that cultural values and practices in many part of the world do not “fit” as naturally with action learning values and practices. Key action learning elements such as diversity of set membership, taking action absent the presence of authority, and frankly sharing the learning experience are more difficult for non-Western cultures to implement. The article concludes with strategies for overcoming these cultural obstacles and steps for building on the synergies of culture in having successful action learning programs in multicultural groups.
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 | 0.000 | 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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