Pelatihan Pemanfaatan Software Pendukung Dalam Pembuatan Artikel Ilmiah Terpublikasi Bagi Guru-Guru SMA
Bibliographic record
Abstract
Supportive software for scientific article creation is an application that assists in writing scientific articlesefficiently and effectively. This software features reference management, note-taking, text review, andformatting according to academic standards. The training to be provided will focus on creating scientificarticles using supportive software to enhance the competence of teachers in publishing their scientific articles.By understanding how to create scientific articles, high school teachers can improve their academic abilities,serve as positive examples for students, provide resources, and contribute to research and educationaldevelopment. This can help improve the quality of education and produce competent and skilled students. Theproposed solution is to organize training on the utilization of supportive software in scientific article creation.This training will provide understanding and skills in using software such as Mendeley and ChatGPT. Mendeleyis a reference management software that assists in collecting, managing, and storing references for scientificarticles. Additionally, Mendeley helps organize references according to various writing styles such as APA,MLA, Chicago, Vancouver, and IEEE. ChatGPT, on the other hand, is a natural language model that helpsgenerate structured and meaningful texts. In the context of scientific article creation, ChatGPT can assist informulating and organizing ideas or concepts, as well as providing suggestions or feedback for scientificwriting. Both of these software options can be chosen based on the needs and preferences of the writer. Theresults of this training, based on a survey using an online mentimeter, showed that it was very useful and theparticipants wanted to continue this training.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.132 | 0.053 |
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".