Perencanaan Strategi Ekspansif dalam Pengelolaan Organisasi Nirlaba Art Music Today
Bibliographic record
Abstract
Organisasi nirlaba membutuhkan perencanaan strategis agar keberlanjutan dari organisasi tersebut tetap ada. Salah satu organisasi nirlaba di bidang seni adalah Art Music Today, yang berdomisili di Yogyakarta. Organisasi ini telah berdiri sepuluh tahun dan memerlukan perencanaan strategis untuk semakin berkembang. Penelitian ini mengevaluasi strategi yang sebelumnya dilakukan Art Music Today dengan menggunakan matriks IE dan SWOT. Hasilnya, strategi yang telah dilakukan Art Music Today bersifat ekspansif, sehingga strategi yang diperlukan selanjutnya bersifat mendukung kemajuan yang telah dilakukan. Salah satu strategi yang dapat diterapkan adalah Blue Ocean, yaitu strategi yang diterapkan di blue ocean , atau ruang pasar yang belum dimanfaatkan tetapi memiliki potensi tinggi. Strategi ini dapat diterapkan dengan implementasi yang dapat dilakukan dengan model 7-S Framework dari McKinsey. The Expansive Strategic Planning for the Management of Art Music Today as a Non-Profit Organization ABSTRACT The non-profit organization needs strategic planning to ensure the organization’s continuity. One of the non-profit organizations in the arts is Art Music Today, which is situated in Yogyakarta, Indonesia. This organization was established in 2012 and requires new strategic planning to improve. This research evaluates past strategies of Art Music Today using IE and SWOT matrixes. The result shows that Art Music Today has been using expansive strategies, so the new strategic planning is designed to boost progress. The Blue Ocean strategy can be used in this; Blue Ocean is a strategy to leverage a niche market with high potential. The strategy can be implemented using a 7-S McKinsey Framework model.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".