Bibliographic record
Abstract
Penelitian dilakukan untuk menganalisis kajian terkait Manajemen Layanan Kepada Masyarakat. Cara mengetahui perkembangan tersebut dengan melakukan analisis terkait perkembangan penelitian manajemen layanan masyarakat. Data terkait penulis yang melakukan kolaborasi penelitian dan siapa penulis yang paling berpengaruh dalam kajian manajemen layanan masyarakat dikaji menggunakan analisis dokumen di Scopus. Sedangkan analisis kata kunci dan penulis yang saling terhubung satu dengan lainnya menggunakan software VosViewer. Untuk melihat penulis yang paling produktif menggunakan tabulasi prisma dari Harzing’s Publish or Perish. Hasil analisis menunjukkan bahwa kajian terkait manajemen layanan masyarakat belum banyak dikaji oleh para peneliti, hal ini dapat dilihat dari hasil screening di Scopus menggunakan kata kunci “Community Service Management”. Hasil menunjukkan hanya terdapat 3 artikel yang membahas terkait kata kunci tersebut. Kesimpulannya adalah penelitian ini masih harus di eksplorasi lebih lanjut mengingat pentingnya kajian terkait pengabdian kepada masyarakat.
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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.061 | 0.131 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".