Peran Manajemen Keragaman Budaya dalam Kinerja Organisasi Non Profit Berbasis Aliansi (Studi Kasus Pada Jakarta Centre For Law Enforcement Cooperation)
Bibliographic record
Abstract
Jakarta Centre for Law Enforcement Cooperation (JCLEC) is the cooperation jointly operates by the Government of Indonesia and the Australian Government. The problem in this study is how cultural diversity management can affect the performance of alliance-based nonprofit organizations. The purpose of this study is to analyze how the application of cultural diversity management consisting of awareness, internal dimensions, external dimensions, leadership, and policy, formed a conducive work environment to create competitive advantage for organizations and influence the organizational performance. The participants of this research were 8 people from Indonesia, Australia and Canada, with positions ranging from Directors to Managers. The diversity of the organizations is from Indonesian National Police (INP), Australian Federal Police (AFP), Royal Canadian Mounted Police (RCMP) combined with the civilian staff from Indonesia and Australia. This study used qualitative method with the case study approach. In-depth interviews, observation, and documentation were used as the research method. The results of the analysis show that the management of cultural diversity consisting of awareness, internal dimensions, external dimensions, leadership, and policies formed a conducive work environment. A conducive working environment where the cultural diversity is accepted and leading to the skill development of the staff is one of the key to create competitive advantage. The competitive advantage which created from the conducive working environment such as creativity and innovation is giving a lot of benefit for organizations and support organizational performance.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".