“Social Information Science” – as a concept for assimilating SmartInternet Usage in a Multi-Cultural Society : The Case of Israel
Bibliographic record
Abstract
The present paper discusses Social Information Science, an innovative field of study, which can enhance assimilation of smart internet usage in multi-cultural countries such as Israel. Social Information Science (S.I.) deals with the development ,theory and applications relating to the retrieval and processing of social and medical information, training “social information scientists,” as well as the development of SI mediation services such as SI banks, SI sections in schools ,public libraries, hospitals, community centers, and private services. Together, these concerted efforts aim to establish a modern information-oriented climate in which stressful social and medical issues are handled through the retrieval and use of reliable information as the basis for knowledgeable decision making. Mediation services demonstrate the potential and risks involved in internet usage, as well as the importance of information-based decisions. Social Information Science will help train people to conduct their daily life decisions on the basis of information selection and self-responsibility- which is a step forward in the evolvement and empowerment the individual.
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 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 | 0.033 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".