DAMPAK PANDEMIK COVID-19 TERHADAP AKTIFITAS MAHASISWA PTS DILINGKUNGAN LLDIKTI 4
Bibliographic record
Abstract
The purpose of this study was to determine the level of impact of the COVID-19 pandemic on PTS student activities in the LLDIKTI 4 environment. So that educational institutions will know the positive and negative impacts of learning that is being carried out at this time. In the COVID-19 pandemic situation, distance learning is still the main choice, although there are still many obstacles experienced by both students and educators. On the other hand, learning during this pandemic provides an extraordinary experience for students and educators in implementing PBM. Educators, especially elderly lecturers who were previously indifferent to information and communication technology (ICT) based learning, are now forced to want to learn. Likewise, Private Universities which were initially still hesitant, slowly began to learn to develop WEB-based Private Higher Education management. The form of online learning in universities in the LLDTI4 environment during the covid 19 pandemic is the use of applications. Applications that are widely used are zoom applications, and google meet. This means that in general PTS in the LLDIKTI4 environment are ready with the facilities and infrastructure along with their human resources. The enthusiasm of students to study in this pandemic condition is quite high, this can be seen from their ability if they have to graduate during a pandemic and later in the field they have to compete with other students who graduated before the pandemic. In general, technology has spread in remote parts of Indonesia, this is a positive aspect of the impact of covid-19, all aspects of life are forced to be ready quickly with technological advances, both government and educational institutions, especially in the field of facilities and infrastructure or human resources. It remains only to fix the lack of intrastructure in remote areas so that the signal and network can be reached properly. The most important lesson from the impact of the COVID-19 pandemic is that learning for certain subjects (not practicum/practice courses) can be carried out online or online with facilities and infrastructure and human resources that are owned by PTS-PTS in the LLDIKTI4 environment even though the pandemic has passed one day. later.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".