PENGARUH PENGGUNAAN PONSEL TERHADAP PERUBAHAN SIKAP PADA REMAJA
Bibliographic record
Abstract
This research is motivated by information and communication technology that has increasingly advanced and developed rapidly in the era of digitalization. One part of information technology is communication technology. The most widely used communication technology is nothing but a cell phone. As advances in increasingly sophisticated technology, information technology has become one of the needs and even demands for rapid exchange of information. Sophisticated technology in this era certainly provides a lot of convenience and flexibility as in doing the things we used to do in everyday life in the real world, now we can do it on our cellphones with features that are no less sophisticated and very easy to we use it. However, although there are many positive aspects that we can take from the advancement of technology in the current era, of course there are also many negative sides or deficiencies of sophisticated technological advancements. One example is with the advance of highly sophisticated communication technology, this makes face-to-face communication decline because everything can be accessed through our respective cellphones. Most mobile phone users are none other than teenagers. According to Indonesiana, cell phones have become a multi-functional tool and are also used as one of their daily needs, in addition to being a communication medium, cellphones are also used as a tool to capture moments, be entertainment (play games), be a social media facility, be a source of information, and others.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".