TINJAUAN BIBLIOMETRIK: MANAJEMEN RISIKO BENCANA, BAHAYA, DAN KERENTANAN DALAM INFRASTRUKTUR TRANSPORTASI
Bibliographic record
Abstract
Peranan strategis infrastruktur transportasi mendorong berbagai penelitian untuk menemukan solusi agar infrastruktur transportasi menjadi lebih kuat, tangguh dan bertahan. Penelitian ini kemudian bertujuan untuk memetakan perkembangan topik riset, gap dan potensi penelitian kedepannya dalam manajemen risiko infrastruktur transportasi. Tinjauan bibliometrik digunakan sebagai kerangka analisis, dengan memanfaatkan basis data Scopus sebagai sumber. Analisis co-occurrence; co-citation; bibliographic coupling; co-authorship; serta analisis klaster dilakukan terhadap 4.231 artikel riset dengan pemetaan secara matematik dan grafis menggunakan software VOSviewer 1.6.19. Hasil analisis menunjukkan artikel riset bidang manajemen risiko infrastruktur transportasi mengalami perkembangan pesat selama delapan tahun terakhir, dengan lebih dari 200 artikel riset dipublikasikan setiap tahunnya. Berbagai artikel riset secara dominan membahas penilaian dan manajemen risiko serta pengelolaan infrastruktur transportasi. Sementara itu, lebih rendahnya pembahasan substansi klaster terakhir yang berkaitan dengan sisi keamanan bagi manusia menjadi gap topik penelitian ini. Peluang penelitian kedepan untuk menemukan solusi lebih efektif adalah mempertimbangkan keamanan manusia dalam pembangunan infrastruktur transportasi sebagai input penelitian.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.049 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".