PRODUKTIVITAS PENGARANG ARTIKEL PADA JURNAL FIHRIS (JURNAL ILMU PERPUSTAKAAN DAN INFORMASI) TAHUN 2006-2014: MENGGUNAKAN HUKUM LOTKA
Bibliographic record
Abstract
Heru Pasuko Rini (12140012), 2016. Author Productivity Fihris Journal (a Journal of Library and Information Science) on 2006-2014: Using the Lotka Law. This research discusses about the application of the Lotka Law on Fihris journal. Lotka law is one of the studies in bibliometric. According to Lotka, if there are 100 people who produce one article, then there will be a quarter people who produce two articles, and so on, with the formula: n works = 1 / n2. Based on Lotka Law, this formula is called the inverse quadrate law. This research also explain about the calculation of K-S test on examining data distribution from two different samples so that the Lotka law can be applied to a particular set of data. The results of this research show that 60 author in Fihris journal produce 99 articles. Based on the Lotka Law formula calculation, it was found that the value of C = 0.93203 and the value of n = 1.91229. Thus, the pattern of productivity in this reserch is ( ) = . . . . From 60 authors, there are 93.203% who produce one article, this means more than half of the authors produce one article. There are 24.761% authors who produce two articles, this means more than quarter of the author who produce on article, and so on. According to the calculation, it can be conclude that the calculation of the theoretical frequency distribution (Lotka Law) in accordance with the theory of Lotka. In the K-S test Dmaks value = 0.43571 and grades K-S = 0.20626, it is seen that the value DmaksK-S. Therefore, the distribution does not meet the theoretical describtion Lotka Law. In the characteristic of authorship, the male authors are more dominant than women authors, the proporsion is 63%:37%, the profession of authors dominated by librarians (42%), and the the author institution are dominated by the authors from outside UIN Sunan Kalijaga, which is 55%:45%. It is recommended for every author to find out the level of their productivity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".