الگوی صیانت از حریم خصوصی اطلاعاتی شهروندان در دولت الکترونیک برای کشورهای در حال توسعه
Bibliographic record
Abstract
The most important aim of this study is to propose a model for protecting citizens’ information privacy in e-Government for developing countries. This study develops theoretical foundations and models of e-Government in judicial-legal dimension. The methodology of this study is qualitative including documentary study, content analysis and comparative study. Statistical population of this study includes 58 countries with information privacy act which 11 countries were selected by judgment sampling for documentary study. These countries include:, England, Canada, France, Germany, Spain, Italy, Norway, Sweden, Ireland, Belgium and Republic of Korea. Proposed model of this study includes seven dimensions: (1) data collection obligations, (2) data use obligations, (3) data retention obligations, (4) data disclosure obligations, (5) data subject rights, (6) controller responsibilities and (7) obligations of accessing to data by citizen. Of 124 identified obligations for protecting citizens’ information privacy in e-Government as indexes of these dimensions, 105 obligations have a weight over 0.5 and can be a pattern for developing countries in order to regulate the e-Government development in the in the field of citizens’ information privacy protection as standard principles. It is hoped that the proposed model of this study would help developing countries in order to protect citizens’ information privacy in e-Government.
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 | 0.003 | 0.005 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".