Managing International Cooperation for Organizational Capacity Development: Setting a Conceptual Foundation for Case Study Research and its Utilization
Bibliographic record
Abstract
Capacity development has been the core of JICA’s technical cooperation, especially after 2000s. The issue has been repeatedly debated among the professional institutions including such as UNDP, JICA and so forth. However, even now, there are not so much articles analyzing the issue from the perspective of management science though some arguments called for the conduct of theory-guided, systematic research about episodes of support for organizations in partner countries. The paper argues and proposes the necessity of a conceptual settings for a case study research and its utilization for systematic learning from the standpoint of management science, particularly public management. It illustrates the conceptual framework by using the knowledge of on-going E-JUST case study. The paper also explore the further steps for strengthening the capacity for organizational development. It proposed “triathlon” approach, namely, conducting the case study research, engagement of professional practitioners through organizational learning and professional development, and vocabulary clarification and integration. Considering the fact that organizational capacity development projects are ex ante novel and ex post unique, the idea of “design references” and “design precedents” are presented for development practitioners to work as “designers” and to create novel solutions in the future.
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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 | 0.047 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.005 |
| 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".