Digital Content Model Framework Based on Social Studies Education
Bibliographic record
Abstract
The growth of the digital world brings positive and also negative influences in the society, For example, the overwhelmed of uneducated material, provoking news, the contents teaches unhealthy behavior, or hoaxes. Most of the people do not have abilities to recognize quality contents or well written contents. Those conditions are really matter, in the 21st century, people must have digital literacy the competencies. In order that the societies will be ready to deal with technology and to address the usefulness of digital content.The community must act as a smart content consumer, and also as a good content producer, so that people have ability to create good digital content and get the benefit of information. However, due to the lack of digital content framework, people have difficulty assessing the quality of digital content, and it is difficult to create content with good criteria. Therefore, it is important to create digital content standards that have a positive goal in the age of technology.To make digital content standards a digital content model was developed which was developed with Research and Development methods, involved students and cyber society on the internet. The digital content framework contains several elements, such as: pillar of social studies education, writing, knowledge, digital media, search engine optimization, and digital copyrights, which will be published in User Generated Content Platform. Furthermore, digital content model framework has been tested and has a useful principle that is used as a guidance for making high quality digital content which considers the virtue of society and the art of state of information technology.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".