Pemanfaatan open access Jurnal Borneo Administrator melalui pengukuran kepuasan pembaca di Lembaga Administrasi Negara
Bibliographic record
Abstract
Open Access Journal (OAJ) is a journal publication that contains articles in full text and user’s can be accessed and used free of charge. The purpose of this study was to determine the extent to which open access was used in the Borneo Administrator Journal (JBA) by measuring reader satisfaction. The research method used was a descriptive quantitative approach. The data collection used a survey method and a data analysis technique used a simple descriptive statistical method. This research has eight research questions, namely the level of user’s satisfaction with access and download facilities, Kemenristek/Dikti accreditation, language, benefit for the profession and career advancement, number of articles and columns, overall format and content, issued quarterly (3x a year), and services provided by JBA managers. The research data analysis has the three highest indicators were published every quarter (3x a year), the format and content as a whole, and were beneficial for professional and career advancement. The lowest score indicators but still in the satisfied category were download facilities, language, and the number of articles and columns. In conclusion, the overall level of reader satisfaction is in the satisfied category. The implementation of open access in the Borneo Administrator Journal is basically to support the development and dissemination of knowledge.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.011 |
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".