Far and creative learning transfer in management development interventions: an ecological triangulation approach to qualitative meta-synthesis
Bibliographic record
Abstract
This meta-synthesis utilized a sample of studies on organization-sponsored management development interventions implemented in United States, Canada, United Kingdom and European organizations. The sample of studies consisted of published articles in peer-reviewed journals and doctoral dissertations. This study explored the following question: What management development interventions have demonstrated what results, in terms of adaptive and/or creative transfer, with what learner characteristics, in what settings, and using what theoretical frameworks? There are three primary interpretations made based on this study. The first is that in order to attain higher (or deeper) levels of learning transfer, managers and their respective organizations must think differently. The second is that it may be beneficial to apply measures focused on the group and organizational units of analysis, and that are extended over the long term using a mixed methodology. Finally, knowledge and skills found at the lower levels of learning transfer (i.e., application) are easily trained utilizing formal methods. Higher levels or deeper levels of learning transfer, however, must be approached holistically and ecologically; that is, the "hard to train" skills are those found at the higher end of the transfer scheme (i.e., interpersonal skills) and involve cognitive, emotional, behavioral, and environmental factors.
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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 | 0.201 | 0.342 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".