Analisis Bibliometrik terhadap program reward pada Scopus menggunakan Biblioshiny
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui perkembangan publikasi ilmiah dalam bidang program yang berlandaskan sistem penghargaan (Reward) selama 2014-2024 menggunakan analisis bibliometrik pada Scopus melalui Biblioshiny. Hasil analisis menunjukkan bahwa topik ini mengalami perkembangan yang signifikan dalam beberapa tahun terakhir, dengan peningkatan jumlah publikasi dan variasi penulis yang berkontribusi, yaitu Pop, C. dari Technical University of Cluj-Napoca, Romania menduduki peringkat pertama sebagai penulis dengan kutipan sebanyak 517 pada artikel berjudul “Blockchain Based Decentralized Management Of Demand Response Programs In Smart Energy Grids” yang diterbit pada jurnal Sensors dari Switzerland. Dilanjut dengan Halpern, SD. dari University of Pennysylvania, Amerika Serikat sebagai peringkat kedua dengan jumlah kutipan 287 pada artikelnya yang berjudul “Randomized Trial of Four Financial-Incentive Programs For Smoking Cessation” pada New England Journal Of Medicine, yang kemudian juga diikuti oleh Patel, MS dari afiliasi yang sama dengan artikelnya yang berjudul “Framing Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults a Randomized, Controlled Trial” dan terbit pada jurnal Annals of Internal Medicine. Produksi karya ilmiah terbanyak dilakukan oleh negara USA dengan total publikasi dokumen berjumlah 1081, UK dengan total 296, China dengan total 185, Canada dengan total 118, dan Australia dengan total 125.
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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 | 0.007 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.046 | 0.083 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".