Social Networking Game as a Way to \nLearn Nations Characteristics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social Networking born since \ninternet was founded. Not only that, base \nhuman characteristic to be make sociality \nwith each other making Social Networking \nbecome popular. Facebook is one of \nSocial Networking in the world. Found by \nMark Zuckenberg at 2004, later Facebook \ngrow bigger and now Facebook became \none of the most popular Social \nNetworking. According to data from \nFacebook, until second quarter of 2013 at \nleast there are about 1,14 billion accounts \non it. \n The main function of \n Social \nNetworking is to socialize people around \nthe world using internet. But now, people \ncan use Social Networking like Facebook \nnot only for meet new friends but also to \nplaying online games. There are a lot of \ngames available on Facebook, like fighting \n, card, adventure and many more. But \nmost of them only point in entertaining. \nTherefore, how if using game on Social \nNetworking to learn. Especially about \nNations Characteristics as culture, \ngeographic, history and many more. So, \npeople in the world know about Nations \nCharacteristics with unique way. Not only \nthat, this way also provide advantages to \nthe Nations itself on the tourism sector. \nFor the players, this is not only to learn \nand have fun, but also can help in \nCharacter Building.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it