Second life e transparência pública: perspectivas para o compartilhamento de dados
Bibliographic record
Abstract
The Information Technology and Communication (ICT) is present in all human daily activities. Digital environments on the Web are being created by public and private organizations to provide information on economics, politics, education, among others, allowing greater visibility of its activities. Thereby, government agencies, using technological resources offered by the Web, are offering public data and information of the public administration at official sites and collaborative environments in order to address the public transparency. Thus, this research set up with the aim to investigate how Second Life (SL) can enhance the access, use and dissemination of public information to promote public transparency. Characteristic of exploratory, descriptive and analytical tried to theoretical foundation in international and national literature on the topics: internet, Web 2.0, collaborative environments, social networks, design elements of Web sites with targeted content focused on public transparency, open data and e-gov. SL were analyzed in environments that provide government information such as the NIC and the library file and NASA CoLab Ontario Careers. In SL, using the tools it offers, a space was created called Green House, in order to gather information about objects and subjects related to public transparency initiatives in Brazil. In this environment were available official government sites and digital books, enabling access of information on the Web in the context of Information Science and the resources offered by the SL platform, we found that the environment is conducive to providing public information and to enhance access to public data, because, in addition to reference information from government agencies, makes it possible for avatars, partially sheltered with their identity, to collaborate with criticisms and suggestions on... (Complete abstract click electronic access below)
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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 | 0.036 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".