Tren Penelitian Etika dan Hak Cipta dalam Perpustakaan: Analisis Bibliometrik
Bibliographic record
Abstract
Ethics and copyright are significant topics within the context of libraries, as libraries play a crucial role as information regulators. This bibliometric study aims to identify research trends related to ethics and copyright in the library context from 2014 to 2024. Data were obtained from the Scopus database using the query term: "((TITLE-ABS-KEY (copyright AND compliance AND in AND libraries) OR TITLE-ABS-KEY (ethical considerations AND for AND librarians) OR TITLE-ABS-KEY (access to information AND copyright AND issues))", resulting in 195 documents analyzed using bibliometric tools like Scopus, Vosviewer, and R (Bibliometrix). The research findings indicate that discussions on ethics and copyright in libraries are prevalent in advanced countries with large populations like United States, India, Canada, and China. It is also observed that the number of publications on this topic has declined since 2014 through 2024. Key journals addressing this topic include Library and Philosophy Practice, PLOS One, and the International Journal of Communication System. Dominant keywords in this study encompass human aspects (human, male, female) and regulatory aspects (article, and copyright). Network mapping results show that keywords are divided into 4 main clusters: information processing cluster, academic library & information access cluster, control & copyright cluster, and management cluster
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.025 | 0.057 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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, 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".