Social Networking Game as a Way to \nLearn Nations Characteristics
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 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".