ANALISIS BIBLIOMETRIC DARI PROGRAM HIBAH (BIBLIOMETRIC OF GRANTS PROGRAM)
Bibliographic record
Abstract
This article is a bibliometric analysis of articles published in Scopus indexed journals that discuss grant programs using Vosviewer. The purpose of bibliometric analysis is aimed at knowing the development of studies related to the grant program. Specifically, this study looks at the development of citations, publication trends, author collaboration, trend terms titles, trend terms author keywords, trend terms abstracts, and state statistics on grant program articles in 2015-2020. Data were collected from the Scopus database using the keyword "grants program". Furthermore, the author uses vosviewer software to analyze and visualize the database obtained. The results showed that the highest number of citations (citations) occurred in 2019 as many as 203 citations. In the publication trend, the most publications occurred in 2019 with 72 articles. Only one author collaborated with three other co-authors namely Kegler M.C. The most used term in the title is 'human' with 143 occurrences. The most used keyword term is 'cash transfer' with 11 passes. The most widely used term in the abstract is 'human' with 143 occurrences. Furthermore, the countries that published the most grant program articles were the United States with 246 articles, Canada with twenty-four articles, and Australia with eighteen articles.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.014 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.126 | 0.167 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".