KAMU HİZMETLERİNDE BİLGİ TEKNOLOJİLERİ UYGULAMALARI: FIRSAT VE TEHDİTLER
Bibliographic record
Abstract
Scientific advancements at the last quarter of 20th century brought “a new and different world” by affecting the existing structures and systems in both business and government. In this framework, Information Technologies (IT) used by states have contributed a lot to public services in many respects. While this encouraging IT transformation is gaining an impetus, “Mass Surveillance Technologies” (MST) used by state agencies pose an important threat on civil rights and liberties. Some examples of corruption in MST usage are strengthening the suspects and worries about the emergence of a Surveillance Society. This study tries to make a comprehensive analysis of pros and cons of IT usage in public services with regard to civil liberties and social structure. In conclusion, the study shows that “protection of privacy” and “advancement of civil liberties” in the 21st century are possible, but the realization of this ideal rests on three conditions; a continuously updated legal structure with strong sanctions; a democratic, open and pluralistic society and a public administration highly committed to this ideal.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".